Paper Digest: ICDE 2022 Papers & Highlights
IEEE International Conference on Data Engineering (ICDE) addresses research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: ICDE 2022 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Online Cardinality Estimation By Self-morphing Bitmaps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper takes a new solution path different from the prior art and proposes a self-morphing bitmap, which combines operational simplicity with structural dynamics, allowing the bitmap to be morphed in a series of steps with an evolving sampling probability that automatically adapts to different stream sizes. |
Haibo Wang; Chaoyi Ma; Shigang Chen; Yuanda Wang; |
| 2 | BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: That is, attackers now can manipulate those relations (i.e., the structure of the graph) to allow some target nodes to evade detection. In this paper, we exploit this vulnerability by designing a new type of targeted structural poisoning attacks to a representative regression-based GAD system termed OddBall. |
Yulin Zhu; Yuni Lai; Kaifa Zhao; Xiapu Luo; Mingquan Yuan; Jian Ren; Kai Zhou; |
| 3 | MDZ: An Efficient Error-bounded Lossy Compressor for Molecular Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (1) We characterize a number of MD datasets and summarize two commonly-used execution models. |
Kai Zhao; Sheng Di; Danny Perez; Xin Liang; Zizhong Chen; Franck Cappello; |
| 4 | Streaming Algorithms for Diversity Maximization with Fairness Constraints Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on the diversity maximization problem with fairness constraints in the streaming setting. |
Yanhao Wang; Francesco Fabbri; Michael Mathioudakis; |
| 5 | SaPHyRa: A Learning Theory Approach to Ranking Nodes in Large Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Sample space Partitloning Hypothesis Ranking, or SaPHyRa, that transforms node rankinginto a hy-pothesis ranking in machine learning. |
Phuc Thai; My T. Thai; Tam Vu; Thang Dinh; |
| 6 | Discovering Representative Attribute-stars Via Minimum Description Length Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. To address these problems, we propose a parameter-free algorithm named CSPM (Compressing Star Pattern Miner) which identifies star-shaped patterns that indicate strong correlations among attributes via the concept of conditional entropy and the minimum description length principle. |
Jiahong Liu; Min Zhou; Philippe Fournier-Viger; Menglin Yang; Lujia Pan; Mourad Nouioua; |
| 7 | Distributed Influence Maximization for Large-Scale Online Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the influence maximization problem on a massive scale, we design distributed algorithms via a cluster of machines, which can effectively speed up the computation while maintaining the state-of-the-art (1 -1/e-c)-approximation guarantee. |
Jing Tang; Yuqing Zhu; Xueyan Tang; Kai Han; |
| 8 | PeriodicSketch: Finding Periodic Items in Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study periodic items in data streams, which refer to those items arriving with a fixed interval. |
Zhuochen Fan; Yinda Zhang; Tong Yang; Mingyi Yan; Gang Wen; Yuhan Wu; Hongze Li; Bin Cui; |
| 9 | Accurate and Scalable Graph Neural Networks for Billion-Scale Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The reason is that they need to propagate and aggregate either node features or predicted labels over the whole graph, which incurs high additional costs relative to the few target nodes. To solve the above challenges, in this paper we propose a novel scalable and effective GNN framework COSAL. |
Juxiang Zeng; Pinghui Wang; Lin Lan; Junzhou Zhao; Feiyang Sun; Jing Tao; Junlan Feng; Min Hu; Xiaohong Guan; |
| 10 | PAW: Data Partitioning Meets Workload Variance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, partitioning methods have been designed to optimize the query performance of partitions with respect to the historical query workload. |
Zhe Li; Man Lung Yiu; Tsz Nam Chan; |
| 11 | Enhancing Federated Learning with In-Cloud Unlabeled Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Herein, upon the federated semi-supervised learning (FSSL) technology, we propose the Ada-FedSemi system, which leverages both on-device labeled data and in-cloud unlabeled data to boost the performance of DL models. |
Lun Wang; Yang Xu; Hongli Xu; Jianchun Liu; Zhiyuan Wang; Liusheng Huang; |
| 12 | GECCO: Constraint-driven Abstraction of Low-level Event Logs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose GECCO, an approach for log abstraction that enables users to impose requirements on the resulting log in terms of constraints. |
Adrian Rebmann; Matthias Weidlich; Han Van Der Aa; |
| 13 | The Stair Sketch: Bringing More Clarity to Memorize Recent Events Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Because the older data is, the less value it has, memorizing recent events with higher accuracy is desirable. To achieve this, we propose a novel data stream processing structure named the Stair sketch. |
Yikai Zhao; Yubo Zhang; Pu Yi; Tong Yang; Bin Cui; Steve Uhlig; |
| 14 | Effective Few-Shot Named Entity Linking By Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. |
Xiuxing Li; Zhenyu Li; Zhengyan Zhang; Ning Liu; Haitao Yuan; Wei Zhang; Zhiyuan Liu; Jianyong Wang; |
| 15 | MPC: Minimum Property-Cut RDF Graph Partitioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Typical partitioning approaches minimize edge-cuts or vertex-cuts. In this paper we argue that these approaches do not avoid or reduce joins between different partitions (i.e., inter-partition join), and propose an approach based on minimizing the number of distinct crossing properties, which we call Minimum Property-Cut (MPC). |
Peng Peng; M. Tamer Özsu; Lei Zou; Cen Yan; Chengjun Liu; |
| 16 | Time-sensitive POI Recommendation By Tensor Completion with Side Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we utilize the side information of social networks and POI locations to enhance the tensor completion model paradigm for more effective time-aware POI recommendation. |
Bo Hui; Da Yan; Haiquan Chen; Wei-Shinn Ku; |
| 17 | Nucleus Decomposition in Probabilistic Graphs: Hardness and Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present nucleus decomposition in probabilistic graphs. |
Fatemeh Esfahani; Venkatesh Srinivasan; Alex Thomo; Kui Wu; |
| 18 | Highly Efficient String Similarity Search and Join Over Compressed Indexes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a flexible framework CSS to reduce the index size and keep high query performance for string search and join applications. |
Guorui Xiao; Jin Wang; Chunbin Lin; Carlo Zaniolo; |
| 19 | Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. |
Hanchen Wang; Ying Zhang; Lu Qin; Wei Wang; Wenjie Zhang; Xuemin Lin; |
| 20 | Fairness-aware Maximal Clique Enumeration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose two novel fairness-aware maximal clique models on attributed graphs, called weak fair clique and strong fair clique respectively. |
Minjia Pan; Rong-Hua Li; Qi Zhang; Yongheng Dai; Qun Tian; Guoren Wang; |
| 21 | Evaluating Complex Queries on Streaming Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present transformation rules and describe query formulation and plan generation for persistent graph queries over streaming graphs. |
Anil Pacaci; Angela Bonifati; M. Tamer Özsu; |
| 22 | Dynamic Functional Dependency Discovery with Dynamic Hitting Set Enumeration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop techniques to dynamically discover FDs in response to changes on data. |
Renjie Xiao; Yong’an Yuan; Zijing Tan; Shuai Ma; Wei Wang; |
| 23 | Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose attentive Knowledge-aware Graph convolutional networks with Collaborative Guidance for personalized Recommendation (CG-KGR). |
Yankai Chen; Yaming Yang; Yujing Wang; Jing Bai; Xiangchen Song; Irwin King; |
| 24 | Continuous Geo-Social Group Monitoring Over Moving Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, in this paper we in-vestigate the problem of continuous geo-social groups monitoring (CGSGM) over moving users. |
Huaijie Zhu; Wei Liu; Jian Yin; Mengxiang Wang; Jianliang Xu; Xin Huang; Wang-Chien Lee; |
| 25 | Constrained Path Search with Submodular Function Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of constrained path search with submodular function maximization (CPS-SM). |
Xuefeng Chen; Xin Cao; Yifeng Zeng; Yixiang Fang; Sibo Wang; Xuemin Lin; Liang Feng; |
| 26 | Academic Expert Finding Via $(k, \mathcal{P})$-Core Based Embedding Over Heterogeneous Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the academic expert finding on heterogeneous graphs by considering the explicit relationships besides the implicit textual semantics of papers in one representation model. |
Xiaoliang Xu; Jun Liu; Yuxiang Wang; Xiangyu Ke; |
| 27 | AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present AutoHEnsGNN, a framework to build effective and robust models for graph tasks without any human intervention. |
Jin Xu; Mingjian Chen; Jianqiang Huang; Xingyuan Tang; Ke Hu; Jian Li; Jia Cheng; Jun Lei; |
| 28 | Reachability-Driven Influence Maximization in Time-dependent Road-social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formally define reachability-driven influence maximization (RDIM) in time-dependent road-social networks, to find a seed set that maximizes the expected influence over potential users, i.e., target users, who are likely to reach a given location within a deadline. |
Yishu Wang; Ye Yuan; Wenjie Zhang; Yi Zhang; Xuemin Lin; Guoren Wang; |
| 29 | Efficient Top-k Ego-Betweenness Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study a problem of finding the top-k vertices with the highest ego-betweennesses. |
Qi Zhang; Rong-Hua Li; Minjia Pan; Yongheng Dai; Guoren Wang; Ye Yuan; |
| 30 | ELDA: Learning Explicit Dual-Interactions for Healthcare Analytics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In healthcare, interactions among medical features (i.e., feature-level interactions) can exhibit different abnormal patterns in detail, while interactions among time steps (i.e., time-level interactions) can indicate the dynamic changes in patients’ health conditions. Therefore, it is necessary to capture and analyze both types of interactions when conducting healthcare analytics, In this paper, we propose a general framework ELDA that is supported by the novel model ELDA-Net to learn dual-interactions for healthcare analytics in an explicit manner. |
Qingpeng Cai; Kaiping Zheng; Beng Chin Ooi; Wei Wang; Chang Yao; |
| 31 | Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first bring forward an analytical framework to generally measure the utilities of LDP mechanisms in high-dimensional space, which can benchmark existing and future LDP mechanisms without conducting any experiment. |
Jiawei Duan; Qingqing Ye; Haibo Hu; |
| 32 | Application-Oriented Workload Generation for Transactional Database Performance Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By carefully studying the application-oriented workload generation problem, we present Transaction Logic and Data Access Distribution to characterize workloads of online transaction processing (OLTP) applications, and propose novel generation algorithms to guarantee the high fidelity of synthetic workloads. |
Luyi Qu; Yuming Li; Rong Zhang; Ting Chen; Ke Shu; Weining Qian; Aoying Zhou; |
| 33 | PRISM: Prefix-Sum Based Range Queries Processing Method Under Local Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on answering range queries while satisfying local differential privacy (LDP). |
Yufei Wang; Xiang Cheng; |
| 34 | Efficient Reinforcement of Bipartite Networks at Billion Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On bipartite networks, ($\alpha, \beta$) -core is a stable structure that ensures different minimum engagement levels of the vertices from different layers, and we aim to reinforce bipartite networks by maximizing the ($\alpha, \beta$) -core. |
Yizhang He; Kai Wang; Wenjie Zhang; Xuemin Lin; Ying Zhang; |
| 35 | Human-Drone Collaborative Spatial Crowdsourcing By Memory-Augmented and Distributed Multi-Agent Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel SC scenario, enabling human participants to work collaboratively with drones in the presence of multiple charging stations to achieve certain data collection tasks, like videography and surveillance. |
Yu Wang; Chi Harold Liu; Chengzhe Piao; Ye Yuan; Rui Han; Guoren Wang; Jian Tang; |
| 36 | SLUGGER: Lossless Hierarchical Summarization of Massive Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the hierarchical graph summarization model, which is an expressive graph representation model that includes the previous one proposed by Navlakha et al. as a special case. |
Kyuhan Lee; Jihoon Ko; Kijung Shin; |
| 37 | DynaHash: Efficient Data Rebalancing in Apache AsterixDB Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce DynaHash, an efficient data rebalancing approach that combines dynamic bucketing with extendible hashing for shared-nothing OLAP-style parallel data management systems. |
Chen Luo; Michael J. Carey; |
| 38 | Efficient Personalized Maximum Biclique Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Apart from online computation algorithms, we explore index-based approaches and propose the PMBC-Index. |
Kai Wang; Wenjie Zhang; Xuemin Lin; Lu Qin; Alexander Zhou; |
| 39 | Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The shortest cycle is a fundamental pattern in graph analytics. In this paper, we investigate the problem of shortest cycle counting for a given vertex in dynamic graphs in light of its applicability to problems such as fraud detection. |
Qingshuai Feng; You Peng; Wenjie Zhang; Ying Zhang; Xuemin Lin; |
| 40 | $O^{2}$-SiteRec: Store Site Recommendation Under The O2O Model Via Multi-graph Attention Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We evaluate our method based on one-month real-world data consisting of 39,465 stores and 23.6 million orders from one of the largest O2O platforms in China. |
Hua Yan; Shuai Wang; Yu Yang; Baoshen Guo; Tian He; Desheng Zhang; |
| 41 | Example-based Spatial Search at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the existing example-based search is not scalable, hindering its applications to larger datasets. To address this challenge, we propose two new algorithms, namely HSP and LORA, to efficiently answer example-based spatial queries. |
Hanyuan Zhang; Siqiang Luo; Jieming Shi; Jing Nathan Yan; Weiwei Sun; |
| 42 | GPU-accelerated Proximity Graph Approximate Nearest Neighbor Search and Construction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel GPU -accelerated algorithm that designs a novel GPU-friendly search framework on proximity graphs to fully exploit the massively parallel processing power of GPUs at key steps of the search. |
Yuanhang Yu; Dong Wen; Ying Zhang; Lu Qin; Wenjie Zhang; Xuemin Lin; |
| 43 | MinIL: A Simple and Small Index for String Similarity Search with Edit Distance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they suffer from a huge space consumption issue when achieving only an acceptable efficiency, especially for long strings. In this paper, we propose a simple yet small index, called minIL, to eliminate this issue. |
Zhong Yang; Bolong Zheng; Xianzhi Wang; Guohui Li; Xiaofang Zhou; |
| 44 | TeGraph: A Novel General-Purpose Temporal Graph Computing Engine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make two key observations: (1) temporal path problems can be described as topological-optimum problems and solved by a universal single scan execution model; and (2) data redundancy commonly occurs in the native format of the transformed temporal graphs, which is unnecessary for information propagation and can be eliminated for better memory utilization and execution efficiency. |
Chengying Huan; Hang Liu; Mengxing Liu; Yongchao Liu; Changhua He; Kang Chen; Jinlei Jiang; Yongwei Wu; Shuaiwen Leon Song; |
| 45 | Clustering-based Partitioning for Large Web Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the property of web graph clustering and propose a novel restreaming algorithm for vertex-cut partitioning. |
Deyu Kong; Xike Xie; Zhuoxu Zhang; |
| 46 | Assessing The Existence of A Function in A Dataset with The G3 Indicator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we examine the computation of g3 with crisp FDs (aka. |
Pierre Faure-Giovagnoli; Jean-Marc Petit; Vasile-Marian Scuturici; |
| 47 | Provenance-aware Discovery of Functional Dependencies on Integrated Views Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose algorithms to speedup the inferred FD discovery process and mine FDs on-the-fly only from necessary data partitions. |
Ugo Comignani; Laure Berti-Equille; Noël Novelli; Angela Bonifati; |
| 48 | Linking Entities Across Relations and Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a notion of parametric simulation to link entities across a relational database $\mathcal{D}$ and a graph $G$. |
Wenfei Fan; Liang Geng; Ruochun Jin; Ping Lu; Resul Tugay; Wenyuan Yu; |
| 49 | EC-Graph: A Distributed Graph Neural Network System with Error-Compensated Compression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these systems may generate high communication costs due to the extensive message passing among graph vertices stored on different machines. To address such limitations, in the paper, 1) we propose a distributed GNN computation system named EC-Graph for CPU clusters, which drastically reduces the communication costs among the machines by message compression; 2) we design a requesting-end compensation method for the embeddings to mitigate the errors induced by compression in the forward propagation and a Bit-Tuner to adaptively balance the model accuracy and message size; and 3) we propose a responding-end compensation approach for the embedding gradients in the backward propagation. |
Zhen Song; Yu Gu; Jianzhong Qi; Zhigang Wang; Ge Yu; |
| 50 | Language-aware Indexing for Conjunctive Path Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present methods to support the full index life cycle: index construction, maintenance, and query processing with our index. |
Yuya Sasaki; George Fletcher; Onizuka Makoto; |
| 51 | COCA: Cost-Effective Collaborative Annotation System By Combining Experts and Amateurs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we combine both experts and amateurs to build a cost-effective data annotation system called COCA. |
Jiayu Lei; Zheng Zhang; Lan Zhang; Xiang-Yang Li; |
| 52 | Reachability Labeling for Distributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main goal of this paper is to design new labeling methods that can work in parallel while producing the same index as TOL. |
Junhua Zhang; Wentao Li; Lu Qin; Ying Zhang; Dong Wen; Lizhen Cui; Xuemin Lin; |
| 53 | DualGraph: Improving Semi-supervised Graph Classification Via Dual Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study semi-supervised graph classification, a fundamental problem in data mining and machine learning. |
Xiao Luo; Wei Ju; Meng Qu; Chong Chen; Minghua Deng; Xian-Sheng Hua; Ming Zhang; |
| 54 | A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose STGNN-DJD, a novel data-driven Spatial-Temporal Graph Neural Network to solve the bike demand and supply prediction problem by unifiedly embedding the Dynamic and Joint ST Dependency in two novel ST graphs. |
Guanyao Li; Xiaofeng Wang; Gunarto Sindoro Njoo; Shuhan Zhong; S.-H. Gary Chan; Chih-Chieh Hung; Wen-Chih Peng; |
| 55 | MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, we argue that these methods suffer from the risk of label sparsity (i.e., the user-item interactions are highly sparse with respect to the feature space), label noise (i.e., the collected user-item interactions are usually noisy), and the underuse of domain knowledge (i.e., the pairwise correlations between samples). To address these challenging problems, we propose a novel Multi-Interest Self-Supervised learning (MISS) framework which enhances the feature embeddings with interest-level self-supervision signals. |
Wei Guo; Can Zhang; Zhicheng He; Jiarui Qin; Huifeng Guo; Bo Chen; Ruiming Tang; Xiuqiang He; Rui Zhang; |
| 56 | Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. |
Yuyan Chen; Yanghua Xiao; Bang Liu; |
| 57 | Maximizing Range Sum in Trajectory Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the definition of MaxRST query where a trajectory is covered by a rectangle if at least one of points in the trajectory is enclosed by the rectangle. |
Kaiqi Zhang; Hong Gao; Xixian Han; Jian Chen; Jianzhong Li; |
| 58 | FedMP: Federated Learning Through Adaptive Model Pruning in Heterogeneous Edge Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We theoretically analyze the impact of pruning ratio on model training performance, and propose to employ a Multi-Armed Bandit based online learning algorithm to adaptively determine different pruning ratios for heterogeneous edge nodes, even without any prior knowledge of their computation and communication capabilities. |
Zhida Jiang; Yang Xu; Hongli Xu; Zhiyuan Wang; Chunming Qiao; Yangming Zhao; |
| 59 | Clustering Activation Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper considers the time-decay scheme in modelling the activeness and proposes a suite of techniques with great effort made on simplification and innovation for efficiency, effectiveness and scalability. |
Zijin Feng; Miao Qiao; Hong Cheng; |
| 60 | Learning Evolvable Time-series Shapelets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we consider when each time-series instance is obtained as progress, and formulate the problem of learning shapelet evolution over progress. |
Akihiro Yamaguchi; Ken Ueo; Hisashi Kashima; |
| 61 | Social Graph Restoration Via Random Walk Sampling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we propose a method for restoring the original social graph from the small sample obtained by a random walk. |
Kazuki Nakajima; Kazuyuki Shudo; |
| 62 | On Inter-operator Data Transfers in Query Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Papers are full of ad hoc use of terms like pipelining and blocking, but these terms are not crisply defined, making it hard to fully understand the results attributed to these concepts. To address this limitation, we introduce a clear terminology for how to think about data transfer between operators in a query pipeline. |
Harshad Deshmukh; Bruhathi Sundarmurthy; Jignesh M. Patel; |
| 63 | Guided Task Planning Under Complex Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a computational framework RL-Planner for TPP. |
Sepideh Nikookar; Paras Sakharkar; Baljinder Smagh; Sihem Amer-Yahia; Senjuti Basu Roy; |
| 64 | Estimating Node Importance Values in Heterogeneous Information Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formally introduce the problem of node importance value estimation in HINs; that is, given the importance values of a subset of nodes in an HIN, we aim to estimate the importance values of the remaining nodes. |
Chenji Huang; Yixiang Fang; Xuemin Lin; Xin Cao; Wenjie Zhang; Maria Orlowska; |
| 65 | Querying Maximum Quasi-independent Set By Pay-and-Recycle Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the paper, we study the problem of computing a maximum quasi-independent set that admits $k$ conflict edges at most but contains the set of query vertices $S$. |
Xiaochen Liu; Weiguo Zheng; Zhenyi Chen; Zhenying He; X. Sean Wang; |
| 66 | Efficient Graph Isomorphism Query Processing Using Degree Sequences and Color-Label Distributions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an efficient algorithm for graph isomorphism query processing. |
Geonmo Gu; Yehyun Nam; Kunsoo Park; Zvi Galil; Giuseppe F. Italiano; Wook-Shin Han; |
| 67 | A Resource-Aware Deep Cost Model for Big Data Query Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The learning-based cost models have been proposed for relational databases, but it does not consider the effect of the available resources. To address this, we propose a resource-aware deep learning model that can automatically predict the execution time of query plans based on historical data. |
Yan Li; Liwei Wang; Sheng Wang; Yuan Sun; Zhiyong Peng; |
| 68 | Maximum Biplex Search Over Bipartite Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the maximum biplex search problem for the first time. |
Wensheng Luo; Kenli Li; Xu Zhou; Yunjun Gao; Keqin Li; |
| 69 | Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a highly efficient approach, named DIG-FL, to estimate the Shapley value of each participant without any model retraining. |
Junhao Wang; Lan Zhang; Anran Li; Xuanke You; Haoran Cheng; |
| 70 | Consistent Answers of Aggregation Queries Via SAT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe the first system able to compute the consistent answers of general aggregation queries with the COUNT ($A$), COUNT (*), and SUM operators, and with or without grouping constructs. |
Akhil A. Dixit; Phokion G. Kolaitis; |
| 71 | Crowdsourced Fact Validation for Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We thereby propose an optimized network flow method which reduces the network complexity to save the time cost by properly grouping the facts. |
Libin Zheng; Peng Cheng; Lei Chen; Jianxing Yu; Xuemin Lin; Jian Yin; |
| 72 | CORE-SG: Efficient Computation of Multiple MSTs for Density-Based Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new computationally efficient approach to compute multiple density-based minimum spanning trees w.r.t. a set of $m_{pts}$ values by leveraging a graph obtained from a single run of the density-based algorithm, without the need for re-runs of the original algorithm. |
Antonio Cavalcante Araujo Neto; Murilo Coelho Naldi; Ricardo J. G. B. Campello; Jörg Sander; |
| 73 | Federated Learning on Non-IID Data Silos: An Experimental Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, to help researchers better understand and study the non-IID data setting in federated learning, we propose comprehensive data partitioning strategies to cover the typical non-IID data cases. |
Qinbin Li; Yiqun Diao; Quan Chen; Bingsheng He; |
| 74 | Bamboo Filters: Make Resizing Smooth Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing related work cannot alleviate both of them. Therefore, we propose a novel AMQ data structure called bamboo filter, which can alleviate the two problems simultaneously. |
Hancheng Wang; Haipeng Dai; Meng Li; Jun Yu; Rong Gu; Jiaqi Zheng; Guihai Chen; |
| 75 | T-Detector: A Trajectory Based Pre-trained Model for Game Bot Detection in MMORPGs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a trajectory based pre-trained model for game bot detection from game character trajectories and mouse trajectories, named T-Detector, which is independent to specific games and can be generalized to others. |
Sha Zhao; Junwei Fang; Shiwei Zhao; Runze Wu; Jianrong Tao; Shijian Li; Gang Pan; |
| 76 | Computing Maximum Structural Balanced Cliques in Signed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel graph reduction technique by transforming the maximum balanced clique problem over a signed graph $G$ to a series of maximum dichromatic clique problems over small subgraphs of $G$. |
Kai Yao; Lijun Chang; Lu Qin; |
| 77 | Black-box Adversarial Attack and Defense on Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a black-box attacker PEEGA, which is restricted to access node features and graph topology for practicability. |
Haoyang Li; Shimin Di; Zijian Li; Lei Chen; Jiannong Cao; |
| 78 | Hybrid Subgraph Matching Framework Powered By Sketch Tree for Distributed Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an efficient hybrid subgraph matching framework that integrates the advantages of both join-based and exploration-based paradigms. |
Yuejia Zhang; Weiguo Zheng; Zhijie Zhang; Peng Peng; Xuecang Zhang; |
| 79 | Maximizing Time-aware Welfare for Mixed Items Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective time-aware utility-driven independent cascade (TUIC) model, that incorporates the time-aware multi-item propagation, utility-driven item adoption, and mixed item relationships together. |
Xiaoye Miao; Huanhuan Peng; Kai Chen; Yuchen Peng; Yunjun Gao; Jianwei Yin; |
| 80 | Unsupervised Matching of Data and Text Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While recent methods achieve promising results for these two tasks, there is no clear solution for the more general problem of matching textual content and structured data. We introduce a framework that supports this new task in an unsupervised setting for any pair of corpora, being relational tables or text documents. |
Naser Ahmadi; Hansjörg Sand; Paolo Papotti; |
| 81 | ImDedup: A Lossless Deduplication Scheme to Eliminate Fine-grained Redundancy Among Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new lossless image deduplication framework to eliminate fine-grained redundancy among images. |
Cai Deng; Qi Chen; Xiangyu Zou; Erci Xu; Bo Tang; Wen Xia; |
| 82 | Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a framework named EMD Globalizer, better suited for the execution of EMD learners on microblog streams. |
Satadisha Saha Bhowmick; Eduard C. Dragut; Weiyi Meng; |
| 83 | Discovering Domain Orders Via Order Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to discover implicit domain orders that we do not already know; for instance, that the order of months in the Chinese Lunar calendar is Corner $<$ Apricot $<$ Peach. |
Reza Karegar; Melicaalsadat Mirsafian; Parke Godfrey; Lukasz Golab; Mehdi Kargar; Divesh Srivastava; Jaroslaw Szlichta; |
| 84 | Accelerating Entity Lookups in Knowledge Graphs Through Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these problems, we represent each entity as an embedding – a compact vector representation that is cognizant of syntactic and semantic similarities and supports fast lookup. We propose, EMBLOOKUP, a novel and efficient approach for learning such an embedding. |
Ghadeer Abuoda; Saravanan Thirumuruganathan; Ashraf Aboulnaga; |
| 85 | MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Yet it remains an open problem how to create a consensus ranking that represents the preferences of all rankers while ensuring fair treatment for candidates with multiple protected attributes such as gender, race, and nationality. In this work, we are the first to define and solve this open Multi-attribute Fair Consensus Ranking (MFCR) problem. |
Kathleen Cachel; Elke Rundensteiner; Lane Harrison; |
| 86 | Hierarchical Core Decomposition in Parallel: From Construction to Subgraph Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the first parallel construction algorithm PHCD for HCD, using a new union-find-based paradigm, and the first parallel algorithm PBKS to search high-quality subgraphs from the hierarchy with respect to various community scoring metrics. |
Deming Chu; Fan Zhang; Wenjie Zhang; Xuemin Lin; Ying Zhang; |
| 87 | Cost-Effective Algorithms for Average-Case Interactive Graph Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the average-case interactive graph search (AIGS) problem that aims to minimize the expected number of queries when the objects follow a probability distribution. |
Qianhao Cong; Jing Tang; Yuming Huang; Lei Chen; Yeow Meng Chee; |
| 88 | Reliable Community Search on Uncertain Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify and study the problem of reliable community search on uncertain graphs (UCS for short). |
Xiaoye Miao; Yue Liu; Lu Chen; Yunjun Gao; Jianwei Yin; |
| 89 | Enhancing Recommendation with Automated Tag Taxonomy Construction in Hyperbolic Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose TaxoRec to jointly construct a tag taxonomy automatically and perform recommendation accurately in hyperbolic space. |
Yanchao Tan; Carl Yang; Xiangyu Wei; Chaochao Chen; Longfei Li; Xiaolin Zheng; |
| 90 | GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. |
Jiabao Jin; Peng Cheng; Lei Chen; Xuemin Lin; Wenjie Zhang; |
| 91 | Triple-Fact Retriever: An Explainable Reasoning Retrieval Model for Multi-hop QA Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There exist two challenges, (1) to fetch the evidence document in an efficient and explainable way at one hop retrieval and (2) to update the question information by aggregating the evidence from the retrieved document after each hop retrieval. To address these two challenges, we propose a triple-fact-based retrieval model to effectively retrieve a related document path in an explainable way for each question. |
Chengmin Wu; Enrui Hu; Ke Zhan; Lan Luo; Xinyu Zhang; Hao Jiang; Qirui Wang; Zhao Cao; Fan Yu; Lei Chen; |
| 92 | A Model-Agnostic Approach for Learning with Noisy Labels of Arbitrary Distributions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a model-agnostic approach for learning with noisy labels of arbitrary distributions. |
Shuang Hao; Peng Li; Renzhi Wu; Xu Chu; |
| 93 | MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel unsupervised anomaly detection method for multivariate time series MAD-SGCN which effectively captures the temporal and spatial correlations of the input sequences simultaneously using Long Short-Term Memory networks (LSTMs) and spectral-based Graph Convolutional Networks (GCNs). |
Panpan Qi; Dan Li; See-Kiong Ng; |
| 94 | BlockOPE: Efficient Order-Preserving Encryption for Permissioned Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present BlockOPE, an efficient OPE scheme designed around the first study integrating OPE into blockchain systems. |
Zhihao Chen; Qingqing Li; Xiaodong Qi; Zhao Zhang; Cheqing Jin; Aoying Zhou; |
| 95 | Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle that, inspired by recent advances of contrastive learning techniques in the computer vision, we propose a novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec). |
Xu Xie; Fei Sun; Zhaoyang Liu; Shiwen Wu; Jinyang Gao; Jiandong Zhang; Bolin Ding; Bin Cui; |
| 96 | Towards Backdoor Attack on Deep Learning Based Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, through analyzing the key factors which influence the effectiveness of a backdoor, we systematize a list of practical principles for designing triggers on time series data. |
Daizong Ding; Mi Zhang; Yuanmin Huang; Xudong Pan; Fuli Feng; Erling Jiang; Min Yang; |
| 97 | On Compressing Temporal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose a framework for compressing emerging temporal graphs based on a dual-representation which articulates both network structure and corresponding temporal information. |
Panagiotis Liakos; Katia Papakonstantinopoulou; Theodore Stefou; Alex Delis; |
| 98 | S-QUERY: Opening The Black Box of Internal Stream Processor State Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we argue that exposing the internal state of streaming systems to outside applications by making it queryable, opens the road for novel use cases. |
Jim Verheijde; Vassilios Karakoidas; Marios Fragkoulis; Asterios Katsifodimos; |
| 99 | Identification for Deep Neural Network: Simply Adjusting Few Weights! Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel watermarking approach that only requires adjusting a few weights, as opposed to prior works that embed watermarks via end-to-end training. |
Yingjie Lao; Peng Yang; Weijie Zhao; Ping Li; |
| 100 | Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and efficient anomaly detection in time series in unsupervised settings. |
Tung Kieu; Bin Yang; Chenjuan Guo; Razvan-Gabriel Cirstea; Yan Zhao; Yale Song; Christian S. Jensen; |
| 101 | HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. |
Tiankai Gu; Chaokun Wang; Cheng Wu; Yunkai Lou; Jingcao Xu; Changping Wang; Kai Xu; Can Ye; Yang Song; |
| 102 | Airphant: Cloud-oriented Document Indexing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on IoU Sketch, we built an end-to-end search engine called Airphant; we describe how Airphant builds, optimizes, and manages IoU Sketch, and ultimately supports keyword-based querying. |
Supawit Chockchowwat; Chaitanya Sood; Yongjoo Park; |
| 103 | Salvaging Failing and Straggling Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel solution to alleviate the failure/straggling problem: use the intermediate results from the partial query execution over available data, and exploit the statistical properties of efficiently partitioned data, particularly, co-hash partitioned data, to provide approximate answers along with confidence bounds. |
Bruhathi Sundarmurthy; Harshad Deshmukh; Paris Koutris; Jeffrey Naughton; |
| 104 | On Time-optimal (k, P)-core Community Search in Dynamic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing cohesive models such as k-core mainly focus on the dense connections inside the community, and neglect the interactions with the vertices outside. In this paper, we study the (k,p) -core community search (KPCS) problem in dynamic graphs, i.e., find the maximal connected subgraph containing a query vertex where each vertex has at least k neighbors and at least p fraction of its neighbors in the subgraph. |
Zhao Lu; Yuanyuan Zhu; Ming Zhong; Jeffrey Xu Yu; |
| 105 | Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Optimal partitioning is computationally expensive, while model-agnostic heuristics are iterative and search over a large solution space. We combine these approaches by using model-agnostic heuristics to improve the partitioning solution from a model-based heuristic. |
Atul Sandur; ChanHo Park; Stavros Volos; Gul Agha; Myeongjae Jeon; |
| 106 | Fairness-Aware Range Queries for Selecting Unbiased Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take initial steps towards integrating fairness conditions into database query processing and data management systems. |
Suraj Shetiya; Ian P. Swift; Abolfazl Asudeh; Gautam Das; |
| 107 | Memorize, Factorize, or Be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models. Therefore, effectively modelling feature interactions has attracted much attention in both the research and industry field. |
Fuyuan Lyu; Xing Tang; Huifeng Guo; Ruiming Tang; Xiuqiang He; Rui Zhang; Xue Liu; |
| 108 | Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a different perspective when addressing the performance challenges. |
Samaa Gazzaz; Vishal Chakraborty; Faisal Nawab; |
| 109 | When Is Early Classification of Time Series Meaningful? (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we make a surprising claim. |
Renjie Wu; Audrey Der; Eamonn J. Keogh; |
| 110 | Current Time Series Anomaly Detection Benchmarks Are Flawed and Are Creating The Illusion of Progress (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we make a surprising claim. |
Renjie Wu; Eamonn J. Keogh; |
| 111 | Who Should Deserve Investment? Attractive Individual and Group Search in Dynamic Information Networks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, no previous work has focused on socialites and active collaborators who are worthy of investment in reality. In this paper, we advocate attractive individuals to model such special objects, and introduce attractive groups to represent groups of well-connected attractive individuals. |
Xinrui Wang; Hong Gao; Zhipeng Cai; Jianzhong Li; |
| 112 | On Efficient Large Maximal Biplex Discovery (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on one kind of cohesive structure, called $k$-biplex, where each vertex of one side is disconnected from at most $k$ vertices of the other side. |
Kaiqiang Yu; Cheng Long; Deepak P; Tanmoy Chakraborty; |
| 113 | A Branch Elimination-based Efficient Algorithm for Large-scale Multiple Longest Common Subsequence Problem (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Searching Longest Common Subsequences (LCS) of Multi-ple (i.e., three or more) sequences, denoted as MLCS for short, is a fundamental problem in computational biology, pattern … |
Shiwei Wei; Yuping Wang; Yiu-ming Cheung; |
| 114 | Efficient and Oblivious Query Processing for Range and KNN Queries (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a practical oblivious query processing framework to enable efficient query processing over a cloud database. |
Zhao Chang; Dong Xie; Feifei Li; Jeff M. Phillips; Rajeev Balasubramonian; |
| 115 | MOSE: A Monotonic Selectivity Estimator Using Learned CDF (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose MOSE, a learning-based MOnotonic Selectivity Estimator, to provide accurate, reliable, and efficient selectivity estimation for query optimization. |
Luming Sun; Cuiping Li; Tao Ji; Hong Chen; |
| 116 | Multiset Membership Lookup in Large Datasets (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We devise compact data structures and lookup algorithms that are amendable for hardware implementation, while guaranteeing high lookup accuracy and supporting interactive query processing. |
Lin Chen; Jihong Yu; |
| 117 | Maximum Signed $\theta$-Clique Identification in Large Signed Graphs (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new clique model for signed graphs, named signed $\theta$-clique. |
Chen Chen; Yanping Wu; Renjie Sun; Xiaoyang Wang; |
| 118 | Stable Community Detection in Signed Social Networks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel model, named stable k-core, to measure the stability of a community in signed graphs by leveraging the concept of balance theory. |
Renjie Sun; Chen Chen; Xiaoyang Wang; Xun Wang; |
| 119 | Hierarchical Representation Learning for Attributed Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks, such as node classification and link prediction, benefits plenty of practical applications. |
Shu Zhao; Ziwei Du; Jie Chen; Yanping Zhang; Jie Tang; Philip S. Yu; |
| 120 | DRGI: Deep Relational Graph Infomax for Knowledge Graph Completion: (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although graph convolutional network (GCN)-based models for knowledge graph embedding have been introduced to address this issue, they still suffer from fact incompleteness, resulting in the unconnectedness of knowledge graph. To solve this problem, we propose a novel model called deep relational graph infomax (DRGI) with mutual information (MI) maximization which takes the benefit of complete structure information and semantic information together. |
Shuang Liang; Jie Shao; Dongyang Zhang; Jiasheng Zhang; Bin Cui; |
| 121 | Continuous Trajectory Similarity Search for Online Outlier Detection (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a new variant of trajectory similarity search from the context of continuous query processing. |
Dongxiang Zhang; Zhihao Chang; Sai Wu; Ye Yuan; Kian-Lee Tan; Gang Chen; |
| 122 | SCPM-CR: A Novel Method for Spatial Co-location Pattern Mining with Coupling Relation Consideration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations in a co-location. |
Peizhong Yang; Lizhen Wang; Xiaoxuan Wang; Lihua Zhou; |
| 123 | Cost-Aware and Distance-Constrained Collective Spatial Keyword Query (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. |
Harry Kai-Ho Chan; Shengxin Liu; Cheng Long; Raymond Chi-Wing Wong; |
| 124 | Graph-Driven Federated Data Management (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional methods for answering queries using views, focused on a rather static setting, fail to address such requirements. To overcome these issues, we propose a full fledged, GLAV-based data integration approach based on graph-based constructs. |
Sergi Nadal; Alberto Abelló; Oscar Romero; Stijn Vansummerem; Panos Vassiliadis; |
| 125 | Network Alignment with Holistic Embeddings (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we pro-pose a novel end-to-end alignment framework that can lever-age different modalities to compare and align network nodes in an efficient way. |
Thanh Trung Huynh; Thang Chi Duong; Thanh Tam Nguyen; Van Vinh Tong; Abdul Sattar; Hongzhi Yin; Quoc Viet Hung Nguyen; |
| 126 | ScaleG: A Distributed Disk-based System for Vertex-centric Graph Processing (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a novel distributed disk-based graph processing system, ScaleG, with a series of user-friendly programming interfaces. |
Xubo Wang; Dong Wen; Lu Qin; Lijun Chang; Wenjie Zhang; |
| 127 | Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a unified Simultaneous Community detection and Singular Value Decomposition (SCSVD) framework for community-aware social recommendation. |
Jiewen Guan; Xin Huang; Bilian Chen; |
| 128 | Core Decomposition on Uncertain Graphs Revisited (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Core decomposition on uncertain graphs is shown to be a key problem in graph analysis. However, existing algorithms for solving this problem are based on a peeling algorithm with … |
Qiangqiang Dai; Rong-Hua Li; Guoren Wang; Rui Mao; Zhiwei Zhang; Ye Yuan; |
| 129 | Multi-Dimensional Randomized Response Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose several complementary approaches to mitigate the dimensionality problem. |
Josep Domingo-Ferrer; Jordi Soria-Comas; |
| 130 | DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Compared with the existing ones, our dataset has larger mesh-grid number, finer-grained mesh size, and higher user sample. Towards such kind of large-scale crowd dataset, we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention mechanism based on Convolutional LSTM. |
Renhe Jiang; Zekun Cai; Zhaonan Wang; Chuang Yang; Zipei Fan; Quanjun Chen; Kota Tsubouchi; Xuan Song; Ryosuke Shibasaki; |
| 131 | TwiCS: Twitter Stream Entity Mention Detection (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a system TwiCS for Entity Mention Detection (EMD) and Entity Detection (ED) in streaming environments. |
Satadisha Saha Bhowmick; Eduard C. Dragut; Weiyi Meng; |
| 132 | A Dual-Store Structure for Knowledge Graphs (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Graph stores are efficient in processing complex knowledge graph queries, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. |
Zhixin Qi; Hongzhi Wang; Haoran Zhang; |
| 133 | Platform-Oriented Event Time Allocation(Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. |
H Sun; N Wang; J Jia; J Huang; H Xiong; L He; X Liu; S Zhang; S Qiao; J Zhao; |
| 134 | Fast Error-Bounded Distance Distribution Computation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, due to the large data volume and expensive distance computation, the exact distance distribution computation is excessively slow. Motivated by this, we present a novel approximate solution in this paper that (i) achieves error-bound guarantees and (ii) is generic to various distance measures. |
Jiahao Zhang; Man Lung Yiu; Bo Tang; Qing Li; |
| 135 | Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by using entity synonym set information and constructing a two-level network. |
Shichao Pei; Lu Yu; Xiangliang Zhang; |
| 136 | Temporal Network Motifs: Models, Limitations, Evaluation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we compare the existing temporal motif models and evaluate the facets of temporal networks that are overlooked in the literature. |
Penghang Liu; Valerio Guarrasi; Ahmet Erdem Sariyüce; |
| 137 | Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose wide and deep message passing network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning. |
Tong Chen; Hongzhi Yin; Jie Ren; Zi Huang; Xiangliang Zhang; Hao Wang; |
| 138 | Efficient EMD-based Similarity Search Via Batch Pruning and Incremental Computation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of EMD-based similarity search, which aims at finding all histogram objects from a dataset whose EMD is within a pre-defined threshold from the given query. |
Yu Chen; Yong Zhang; Jin Wang; Jiacheng Wu; Chunxiao Xing; |
| 139 | Building Graphs at Scale Via Sequence of Edges: Model and Generation Algorithms (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the first sequence-of-edges model, denoted as temporal preferential attachment (TPA). |
Yu Liu; Lei Zou; Zhewei Wei; |
| 140 | Finding Critical Users in Social Communities Via Graph Convolutions (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve the performance, we propose a novel learning-based heuristic. |
Kangfei Zhao; Zhiwei Zhang; Yu Rong; Jeffrey Xu Yu; Junzhou Huang; |
| 141 | On The Fairness of Time-Critical Influence Maximization in Social Networks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, in many applications, the spread of influence is time-critical, i.e., it is only beneficial to be influenced before a deadline. As we show in this paper, such time-criticality of information could further exacerbate the disparity of influence across groups. |
Junaid Ali; Mahmoudreza Babaei; Abhijnan Chakraborty; Baharan Mirzasoleiman; Krishna P. Gummadi; Adish Singla; |
| 142 | Fast Reachability Queries Answering Based on RCN Reduction (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel graph reduction approach, namely RCN reduction, to reduce the input graph $G$ of $\vert V\vert$ nodes into a smaller one with $\vert V^{r}\vert$ nodes. |
Junfeng Zhou; Jeffrey Xu Yu; Yaxian Qiu; Xian Tang; Ziyang Chen; Ming Du; |
| 143 | GloDyNE: Global Topology Preserving Dynamic Network Embedding (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, this kind of approximation, although can improve efficiency, cannot effectively preserve the global topology of a dynamic network at each timestep, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To address this issue, we propose a new DNE method for better global topology preservation. |
Chengbin Hou; Han Zhang; Shan He; Ke Tang; |
| 144 | Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose and investigate the top-k vulnerable nodes detection problem in uncertain graphs. |
Dawei Cheng; Chen Chen; Xiaoyang Wang; Sheng Xiang; |
| 145 | A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a prefix-preserving encryption based data outsourcing framework which is applicable to multiple different types of data, such as geo-locations, market basket data, DNA sequences, numerical data and timestamps. |
Shangyu Xie; Meisam Mohammady; Han Wang; Lingyu Wang; Jaideep Vaidya; Yuan Hong; |
| 146 | Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose privacy-preserving user-based CF protocols using the BGV fully homomorphic encryption scheme, named BGV-CF and optimized BGV-CF (OBGV-CF), in order to protect privacy of users in recommender systems. |
Seiya Jumonji; Kazuya Sakai; Min-Te Sun; Wei-Shinn Ku; |
| 147 | Event Popularity Prediction Using Influential Hashtags from Social Media (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It has been widely inves-tigated by existing approaches focusing on predicting single attribute occurrences which are not comprehensive enough for representing complex social event propagation. Motivated by this, we propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. |
Xi Chen; Xiangmin Zhou; Jeffrey Chan; Lei Chen; Timos Sellis; Yanchun Zhang; |
| 148 | Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen comparing four of the most popular existing approaches including Google Maps. |
Lingxiao Li; Muhammad Aamir Cheema; Hua Lu; Mohammed Eunus Ali; Adel N. Toosi; |
| 149 | K-Pleased Querying (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we point out some issues in $k$ – Regret Querying, including the assumption of non-negative dataset and the lack of shift invariance. |
Zitong Chen; Ada Wai-Chee Fu; Cheng Long; Yang Wu; |
| 150 | Efficient Approximate Range Aggregation Over Large-scale Spatial Data Federation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the first-of-its-kind approximate algorithms for efficient range aggregation over spatial data federation. |
Yexuan Shi; Yongxin Tong; Yuxiang Zeng; Zimu Zhou; Bolin Ding; Lei Chen; |
| 151 | Lasagne: A Multi-Layer Graph Convolutional Network Framework Via Node-aware Deep Architecture (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Lasagne, a novel multi-layer graph convolutional network (GCN) framework to over-come the over-smoothing problem and realize the full poten-tials of deep GCNs. |
Xupeng Miao; Wentao Zhang; Yingxia Shao; Bin Cui; Lei Chen; Ce Zhang; Jiawei Jiang; |
| 152 | Recurrent Learning on $\text{PM}_{2.5}$ Prediction Based on Clustered Airbox Dataset: Extended Abstract Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a $\mathbf{PM}_{2.5}$ prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. |
Chia-Yu Lo; Wen-Hsing Huang; Ming-Feng Ho; Min-Te Sun; Ling-Jyh Chen; Kazuya Sakai; Wei-Shinn Ku; |
| 153 | Frequency Estimation in Data Streams: Learning The Optimal Hashing Scheme (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. |
Dimitris Bertsimas; Vassilis Digalakis; |
| 154 | CoANE: Modeling Context Co-occurrence for Attributed Network Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node’s neighborhood should be not only depicted by multi-hop nodes, but consider certain social circles. To model such information, in this paper, we propose a novel ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE). |
I-Chung Hsieh; Cheng-Te Li; |
| 155 | Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. |
Fanwei Zhu; Yuan Fang; Kai Zhang; Kevin Chen-Chuan Chang; Hongtai Cao; Zhen Jiang; Minghui Wu; |
| 156 | Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, so that the discriminator can perform neighborhood aggregation on the fake samples to learn superior representations. |
Zemin Liu; Yuan Fang; Yong Liu; Vincent W. Zheng; |
| 157 | GQP: A Framework for Scalable and Effective Graph Query-based Pricing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a framework GQP for pricing graph data on the data market platform. |
Chen Chen; Ye Yuan; Zhenyu Went; Guoren Wang; Anteng Li; |
| 158 | Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To approach the challenges of non-IID data and limited communication resource raised by the emerging federated learning (FL) in mobile edge computing (MEC), we propose an efficient framework, called FedMigr, which integrates a deep reinforcement learning (DRL) based model migration strategy into the pioneer FL algorithm FedAvg. |
Jianchun Liu; Yang Xu; Hongli Xu; Yunming Liao; Zhiyuan Wang; He Huang; |
| 159 | From Batch Processing to Real Time Analytics: Running Presto® at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we’d like to introduce some of the most important features and performance improvements the open source Presto community made in recent years, which enables companies running Presto at scale, supporting millions of queries per day, with hundreds of thousands of machines. |
Zhenxiao Luo; Lu Niu; Venki Korukanti; Yutian Sun; Masha Basmanova; Yi He; Beinan Wang; Devesh Agrawal; Hao Luo; Chunxu Tang; Ashish Singh; Yao Li; Peng Du; Girish Baliga; Maosong Fu; |
| 160 | Computation Reuse Via Fusion in Amazon Athena Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we build upon recent work and introduce new optimizations in Athena that handle some common expression scenarios without materializing intermediate results or duplicating work. |
Nicolas Bruno; Johnny Debrodt; Chujun Song; Wei Zheng; |
| 161 | Dynamic Hypergraph Convolutional Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore a dynamic HCN based on the attention mechanism (DyHCN) for time series prediction. |
Nan Yin; Fuli Feng; Zhigang Luo; Xiang Zhang; Wenjie Wang; Xiao Luo; Chong Chen; Xian-Sheng Hua; |
| 162 | Equi-Joins Over Encrypted Data for Series of Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a new encryption scheme that can efficiently perform equi-joins over encrypted data using only software and a single server with better security than the state-of-the-art. |
Masoumeh Shafieinejad; Suraj Gupta; Jin Yang Liu; Koray Karabina; Florian Kerschbaum; |
| 163 | Efficiently Transforming Tables for Joinability Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Compared to a state-of-the-art approach, our algorithm covers every transformation that is covered in the state-of-the-art approach but is a few orders of magnitude faster, as evaluated on various real and synthetic data. |
Arash Dargahi Nobari; Davood Rafiei; |
| 164 | Near Data Processing in Taurus Database Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes the design and implementation of near data processing (NDP) in Taurus. |
Shu Lin; Arunprasad P. Marathe; Per-Åke Larson; Chong Chen; Calvin Sun; Paul Lee; Weidong Yu; Jianwei Li; Juncai Meng; Roulin Lin; Xiaoyang Chenxi; Qingping Zhuxii; |
| 165 | Regular Path Query Evaluation Sharing A Reduced Transitive Closure Based on Graph Reduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel concept of RPQ-based graph reduction, which significantly simplifies the original graph through edge-level and vertex-level reductions. |
Inju Na; Yang-Sae Moon; Ilyeop Yi; Kyu-Young Whang; Soon J. Hyun; |
| 166 | Bilateral Preference-aware Task Assignment in Spatial Crowdsourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, they are not appropriate to the specific applications, such as carpool. Inspired by this, we investigate an interesting problem of task assignment, namely bilateral preference-aware task assignment (BPTA), with the goal of maximizing the overall satisfaction of workers and tasks by assigning tasks to suitable workers based on their routine trajectories. |
Xu Zhou; Shiting Liang; Kenli Li; Yunjun Gao; Keqin Li; |
| 167 | TMN: Trajectory Matching Networks for Predicting Similarity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We evaluate various approaches on real-life datasets under extensive trajectory distance metrics. |
Peilun Yang; Hanchen Wang; Defu Lian; Ying Zhang; Lu Qin; Wenjie Zhang; |
| 168 | Deep Popularity Prediction in Multi-Source Cascade with HERI-GCN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework, called HEterogeneous Recurrent Integrated Graph Convolutional Neural Network (HERI-GCN). |
Zhen Wu; Jingya Zhou; Ling Liu; Chaozhuo Li; Fei Gu; |
| 169 | Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, recovery attack has not been well studied in the current literature. To tackle these issues, we propose a frequency-based randomization model with a rigorous differential privacy guarantee for trajectory data publishing. |
Fengmei Jin; Wen Hua; Boyu Ruan; Xiaofang Zhou; |
| 170 | Scape: Scalable Collaborative Analytics System on Private Database with Malicious Security Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose, analyze, and implement Scape, a Scalable Collaborative Analytics system on Private databasE with malicious security. |
Feng Han; Lan Zhang; Hanwen Feng; Weiran Liu; Xiangyang Li; |
| 171 | HET-KG: Communication-Efficient Knowledge Graph Embedding Training Via Hotness-Aware Cache Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This becomes a main obstacle to apply these existing models to large-scale knowledge graphs. To address this challenge, we propose HET-KG, a distributed system for training knowledge graph embedding efficiently. |
Sicong Dong; Xupeng Miao; Pengkai Liu; Xin Wang; Bin Cui; Jianxin Li; |
| 172 | Subspace Embedding Based New Paper Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To take into account information about academic networks for paper recommendation, we propose a graph convolutional neural method to combine the paper content with other related elements, where user interests and academic influences are modeled asymmetric. |
Yi Xie; Wen Li; Yuqing Sun; Elisa Bertino; Bin Gong; |
| 173 | IPS: Instance Profile for Shapelet Discovery for Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We have identified two main issues of such an adoption: 1) discords as “shapelets”, and 2) lack of shapelet diversity. In response to these issues, we propose instance profile for shapelets, called IPS, for shapelet discovery for TSC. |
Guozhong Li; Byron Choi; Jianliang Xu; Sourav S Bhowmick; Daphne Ngar-yin Mah; Grace L.H. Wong; |
| 174 | A Comparative Study of In-Database Inference Approaches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the three most representative ones and study their advantages and limitations. |
Qiuru Lin; Sai Wu; Junbo Zhao; Jian Dai; Feifei Li; Gang Chen; |
| 175 | Ubiquitous Verification in Centralized Ledger Database Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce verification principles behind LedgerDB, a centralized ledger database that achieves both strong external auditability and fast verification. |
Xinying Yang; Sheng Wang; Feifei Li; Yuan Zhang; Wenyuan Yan; Fangyu Gai; Benquan Yu; Likai Feng; Qun Gao; Yize Li; |
| 176 | OLxPBench: Real-time, Semantically Consistent, and Domain-specific Are Essential in Benchmarking, Designing, and Implementing HTAP Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents OLxPBench, a composite HTAP benchmark suite. |
Guoxin Kang; Lei Wang; Wanling Gao; Fei Tang; Jianfeng Zhan; |
| 177 | Dynamic Approximate Maximum Independent Set on Massive Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose a framework that maintains a $(\displaystyle \frac{\triangle}{2}+1)$ -approximate MaxIS over dynamic graphs and prove that it achieves a constant approximation ratio in many real-world networks. |
Xiangyu Gao; Jianzhong Li; Dongjing Miao; |
| 178 | Rank-Regret Minimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In HD space, we propose an algorithm HDRRM that introduces a double approximation guarantee on rank-regret. |
Xingxing Xiao; Jianzhong Li; |
| 179 | An Extended SSD-Based Cache for Efficient Object Store Access in SAP IQ Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we describe our experience with the design and implementation of the ECM, in particular, the design choices we had to make to overcome the fact that, when compared to object stores, SSDs have a much more limited I/O bandwidth. |
Sagar Shedge; Nishant Sharma; Anant Agarwal; Mohammed Abouzour; Güneş Aluç; |
| 180 | Robust Attributed Network Embedding Preserving Community Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Rather than using pairwise connection-based micro-structure, we try to guide the node embedding by the underlying community structure learned from data itself as an unsupervised learning, as to own stronger anti-interference ability. |
Yunfei Liu; Zhen Liu; Xiaodong Feng; Zhongyi Li; |
| 181 | Maximal Balanced Signed Biclique Enumeration in Signed Bipartite Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is critical to leverage signed information to better characterize biclique. To fill this gap, in this paper, we propose a novel biclique model, named balanced signed biclique, by leveraging the property of balance theory. |
Renjie Sun; Yanping Wu; Chen Chen; Xiaoyang Wang; Wenjie Zhang; Xuemin Lin; |
| 182 | Maximal Directed Quasi -Clique Mining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we generalize the concept of quasi-cliques to directed graphs by proposing (γ1, γ2) -quasi-cliques which have density requirements in both inbound and outbound directions of each vertex in a quasi-clique subgraph. |
Guimu Guo; Da Yan; Lyuheng Yuan; Jalal Khalil; Cheng Long; Zhe Jiang; Yang Zhou; |
| 183 | EMP: Max-P Regionalization with Enriched Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such a major enrichment introduces several challenges in both feasibility and scalability. To address these challenges, we propose the FaCT algorithm, a three-phase greedy approach that finds a feasible set of spatial regions that satisfy EMP constraints while supporting large datasets compared to the existing literature. |
Yunfan Kang; Amr Magdy; |
| 184 | VChain+: Optimizing Verifiable Blockchain Boolean Range Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new searchable blockchain system, vChain+, that supports efficient verifiable boolean range queries with additional features. |
Haixin Wang; Cheng Xu; Ce Zhang; Jianliang Xu; Zhe Peng; Jian Pei; |
| 185 | Finding Top-r Influential Communities Under Aggregation Functions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We give a theoretical analysis demonstrating the hardness of the problems and propose efficient and effective heuristic solutions for our top-r influential community search problems. |
You Peng; Song Bian; Rui Li; Sibo Wang; Jeffrey Xu Yu; |
| 186 | Efficient $k-\text{clique}$ Listing with Set Intersection Speedup Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose two efficient algorithms SDegree and BitCol for k-clique listing. |
Zhirong Yuan; You Peng; Peng Cheng; Li Han; Xuemin Lin; Lei Chen; Wenjie Zhang; |
| 187 | Distributed Set Label-Constrained Reachability Queries Over Billion-Scale Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Set label-constrained reachability (SLCR) query in edge-labeled graphs is a building block of many graph-based applications. Formally, given two sets $S$ and $T$ of source and … |
Yuanyuan Zeng; Wangdong Yang; Xu Zhou; Guoqing Xiao; Yunjun Gao; Kenli Li; |
| 188 | Efficient Learning-based Community-Preserving Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel community-preserving generative adversarial network (CPGAN) for effective and efficient (scalable) graph simulation. |
Sheng Xiang; Dawei Cheng; Jianfu Zhang; Zhenwei Ma; Xiaoyang Wang; Ying Zhang; |
| 189 | Adaptive Code Learning for Spark Configuration Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is infeasible for approaches such as Bayesian Optimization and Reinforcement Learning to collect sufficient training instances or repeatedly execute the applications; (C3) Spark supports various analytical applications and the tuning system needs to adapt to different applications. To address these challenges, we propose a LIghtweighT knob rEcommender system (LITE) for auto-tuning Spark configurations on various analytical applications and large-scale datasets. |
Chen Lin; Junqing Zhuang; Jiadong Feng; Hui Li; Xuanhe Zhou; Guoliang Li; |
| 190 | Collecting Triangle Counts with Edge Relationship Local Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But the LDP notion does not consider data correlations. With the understanding of these limitations, we introduce Edge Relationship Local Differential Privacy (Edge-RLDP), which provides a strong privacy guarantee as LDP and considers data correlations simultaneously. |
Yuhan Liu; Suyun Zhao; Yixuan Liu; Dan Zhao; Hong Chen; Cuiping Li; |
| 191 | ITemporal: An Extensible Generator of Temporal Benchmarks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper contributes iTemporal, an extensible generator of temporal benchmarks. |
Luigi Bellomarini; Markus Nissl; Emanuel Sallinger; |
| 192 | Self-Supervised Dual-Channel Attentive Network for Session-based Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to address the information loss issue in SSR, we propose a novel Dual-Channel Attentive Network (DCAN) to leverage both sequential infor-mation and complex item transitions. |
Liuyin Wang; Xianghong Xu; Kai Ouyang; Huanzhong Duan; Yanxiong Lu; Hai-Tao Zheng; |
| 193 | XTree: Traversal-Based Partitioning for Extreme-Scale Graph Processing on Supercomputers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents XTree, an efficient traversal-based partitioning method for minimizing communication overhead of graph processing on supercomputers. |
Xinbiao Gan; Yiming Zhang; Ruigeng Zeng; Jie Liu; Ruibo Wang; Tiejun Li; Li Chen; Kai Lu; |
| 194 | Deep and Collective Entity Resolution in Parallel Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a fixpoint model for deep and collective ER, by chasing with logic rules that are collectively defined across multiple relations and may embed machine learning classifiers for ER as predicates. |
Ting Deng; Wenfei Fan; Ping Lu; Xiaomeng Luo; Xiaoke Zhu; Wanhe An; |
| 195 | RW-Tree: A Learned Workload-aware Framework for R-tree Construction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Third, both range queries and kNN queries should be considered in the workload. To address these challenges, we propose a novel framework that leverages a learning-based method to solve the workload-aware R-tree construction problem. |
Haowen Dong; Chengliang Chai; Yuyu Luo; Jiabin Liu; Jianhua Feng; Chaoqun Zhan; |
| 196 | Spatial-Temporal Interval Aware Sequential POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we exploit two lightweight approaches, Time Aware Position Encoder (TAPE) and Interval Aware Attention Block (IAAB), to impel SAN by considering the spatial-temporal intervals among POIs separately, where requiring neither extra parameters nor high computational cost. |
En Wang; Yiheng Jiang; Yuanbo Xu; Liang Wang; Yongjian Yang; |
| 197 | Self-reconstructive Evidential Clustering for High-dimensional Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The adoption of hard/fuzzy partition ignores the ambiguity in the assignment of objects and may lead to performance degradation. To address these issues, we propose a novel self-reconstructive evidential clustering (SREC) algorithm. |
Chaoyu Gong; Yongbin Li; Di Fu; Yong Liu; Pei-hong Wang; Yang You; |
| 198 | Joint Evidential $K$-Nearest Neighbor Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this way, embedding the learned metric directly into the K-NN does not efficiently improve its accuracy. To address these issues, we propose a joint K-NN algorithm with the help of evidence theory, optimizing the joint learning of adaptive $K$ and distance matrix based on the feedback from error function. |
Chaoyu Gong; Yongbin Li; Yong Liu; Pei-hong Wang; Yang You; |
| 199 | Semantics Driven Embedding Learning for Effective Entity Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the fundamental problem in knowledge base integration, i.e., entity alignment (EA). |
Ziyue Zhong; Meihui Zhang; Ju Fan; Chenxiao Dou; |
| 200 | Influence-aware Task Assignment in Spatial Crowdsourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Next, we propose a Random reverse reachable-based Propagation Optimization algorithm that exploits reverse reachable sets to calculate the probability of workers being informed about tasks in a social network. Based on worker-task influence derived from the above three factors, we propose three influence-aware task assignment algorithms that aim to maximize the number of assigned tasks and worker-task influence. |
Xuanhao Chen; Yan Zhao; Kai Zheng; Bin Yang; Christian S. Jensen; |
| 201 | Competitive Consistent Caching for Transactions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To further exploit batching, we propose to reorder transactions within batches while guaranteeing that each transaction sees data values with bounded staleness. |
Shuai An; Yang Cao; Wenyue Zhao; |
| 202 | PSP: Progressive Space Pruning for Efficient Graph Neural Architecture Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the fact that the search space plays a critical role in the NAS, we propose a novel and effective graph neural architecture search method called PSP from the perspective of search space design in this paper. |
Guanghui Zhu; Wenjie Wang; Zhuoer Xu; Feng Cheng; Mengchuan Qiu; Chunfeng Yuan; Yihua Huang; |
| 203 | Fluid: Dataset Abstraction and Elastic Acceleration for Cloud-native Deep Learning Training Jobs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Fluid, a cloud-native platform that provides DL training jobs with a data abstraction called Fluid Dataset to access training data from heterogeneous sources in a unified manner with transparent and elastic data acceleration powered by auto-tuned cache runtimes. |
Rong Gu; Kai Zhang; Zhihao Xu; Yang Che; Bin Fan; Haojun Hou; Haipeng Dai; Li Yi; Yu Ding; Guihai Chen; Yihua Huang; |
| 204 | AutoIndex: An Incremental Index Management System for Dynamic Workloads Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, we propose a deep index estimation model, which integrates the practical experience to extract critical cost features and applies deep regression to estimate index benefits from historical index management data. |
Xuanhe Zhou; Luyang Liu; Wenbo Li; Lianyuan Jin; Shifu Li; Tianqing Wang; Jianhua Feng; |
| 205 | Cross-Domain Recommendation to Cold-Start Users Via Variational Information Bottleneck Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we consider a key point of the CDR task: what information needs to be shared across domains? |
Jiangxia Cao; Jiawei Sheng; Xin Cong; Tingwen Liu; Bin Wang; |
| 206 | Zoomer: Boosting Retrieval on Web-scale Graphs By Regions of Interest Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Zoomer, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. |
Yuezihan Jiang; Yu Cheng; Hanyu Zhao; Wentao Zhang; Xupeng Miao; Yu He; Liang Wang; Zhi Yang; Bin Cui; |
| 207 | Distributed Task-Based Training of Tree Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we target the exact training of tree models by effectively utilizing the available CPU cores. |
Da Yan; Md Mashiur Rahman Chowdhury; Guimu Guo; Jalal Kahlil; Zhe Jiang; Sushil Prasad; |
| 208 | DB-LSH: Locality-Sensitive Hashing with Query-based Dynamic Bucketing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent studies to address these issues often incur extra overhead to identify eligible candidates or remove false positives, making query time no longer sub-linear. To address this dilemma, in this paper we propose a novel LSH scheme called DB-LSH which supports efficient ANN search for large high-dimensional datasets. |
Yao Tian; Xi Zhao; Xiaofang Zhou; |
| 209 | Clock-G: A Temporal Graph Management System with Space-efficient Storage Technique Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we discuss the design of a temporal graph management system Clock-G and introduce a new space-efficient storage technique δ-Copy+Log, Clock-G is designed by the devel-opers of the Thing’in platform and is currently being deployed into production. |
Maria Massri; Zoltan Miklos; Philippe Raipin; Pierre Meye; |
| 210 | Provenance in Temporal Interaction Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For each policy, we propose space- and time-efficient meta-data propagation mechanisms for continuously tracking provenance at vertices. |
Chrysanthi Kosyfaki; Nikos Mamoulis; |
| 211 | Discovering Hierarchy of Bipartite Graphs with Cohesive Subgraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the bipartite hierarchy, which is the first model to discover the hierarchical structure of bipartite graphs based on the concept of $(\alpha_{2}\beta){-}$ core and graph connectivity. |
Kai Wang; Wenjie Zhang; Xuemin Lin; Ying Zhang; Shunyang Li; |
| 212 | TraSS: Efficient Trajectory Similarity Search Based on Key-Value Data Stores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, this paper proposes TraSS, an efficient framework for trajectory similarity search in key-value data stores. |
Huajun He; Ruiyuan Li; Sijie Ruan; Tianfu He; Jie Bao; Tianrui Li; Yu Zheng; |
| 213 | Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a new problem, namely personalized graph summarization, where the objective is to obtain a summary graph where more emphasis is put on connections closer to a given set of target nodes. |
Shinhwan Kang; Kyuhan Lee; Kijung Shin; |
| 214 | Efficient Computation of Cohesive Subgraphs in Uncertain Bipartite Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the ($\alpha,\beta,\eta$)-core model, which is the first cohesive subgraph model on uncertain bipartite graphs. |
Gengda Zhao; Kai Wang; Wenjie Zhang; Xuemin Lin; Ying Zhang; Yizhang He; |
| 215 | On Maximising The Vertex Coverage for ${\text{Top}}-k$ T-Bicliques in Bipartite Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a new biclique problem, called the top-k t-biclique coverage problem. |
Aman Abidi; Lu Chen; Chengfei Liu; Rui Zhou; |
| 216 | Synthesizing Privacy Preserving Entity Resolution Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a new problem of synthesizing surrogate ER datasets using transformer models, with the goal that the ER model trained on the synthesized dataset can be used directly on the real dataset. |
Xuedi Qinl; Chengliang Chai; Nan Tang; Jian Li; Yuyu Luo; Guoliang Li; Yaoyu Zhu; |
| 217 | Workload-Aware Shortest Path Distance Querying in Road Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Workload-aware Core-Forest label index (WCF) to exploit spatial skew in workloads. |
Bolong Zheng; Jingyi Wan; Yongyong Gao; Yong Ma; Kai Huang; Xiaofang Zhou; Christian S. Jensen; |
| 218 | BETZE: Benchmarking Data Exploration Tools with (Almost) Zero Effort Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose BETZE, a benchmark generator to evaluate the performance of data exploration solutions for semi-structured data. |
Nico Schäfer; Sebastian Michel; |
| 219 | Evolutionary Clustering of Moving Objects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the notion of evolutionary clustering of moving objects, abbreviated ECM, that enhances the quality of moving object clustering by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. |
Tianyi Li; Lu Chen; Christian S. Jensen; Torben Bach Pedersen; Yunjun Gao; Jilin Hu; |
| 220 | Temporal Regular Path Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider temporal property graphs (TPGs) and propose temporal regular path queries (TRPQs) that incorporate time into TPG navigation. |
Marcelo Arenas; Pedro Bahamondes; Amir Aghasadeghi; Julia Stoyanovich; |
| 221 | A Machine Learning-Aware Data Re-partitioning Framework for Spatial Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: If the granularity of the grid is too fine, that results in a large number of grid cells leading to long training time and high memory consumption issues during the model training. To alleviate this problem, we propose a machine learning-aware spatial data re-partitioning framework that substantially reduces the granularity of the spatial grid. |
Kanchan Chowdhury; Venkata Vamsikrishna Meduri; Mohamed Sarwat; |
| 222 | Improving Fairness for Data Valuation in Horizontal Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. |
Zhenan Fan; Huang Fang; Zirui Zhou; Jian Pei; Michael P. Friedlander; Changxin Liu; Yong Zhang; |
| 223 | DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DP AR2, a fast and scalable PARAFAC2 decomposition method for irregular dense tensors. |
Jun-Gi Jang; U Kang; |
| 224 | Apache ShardingSphere: A Holistic and Pluggable Platform for Data Sharding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents Apache ShardingSphere, the first top-level open-source platform for data sharding in Apache, which enables developers to use sharded databases like one database. |
Ruiyuan Li; Liang Zhang; Juan Pan; Junwen Liu; Peng Wang; Nianjun Sun; Shanmin Wang; Chao Chen; Fuqiang Gu; Songtao Guo; |
| 225 | Conditional Regression Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to automatically discover the regression models that apply conditionally to only a part of the data, namely conditional regression rules (CRRs), enlightened by the conditional functional dependencies (CFDs) that are FDs hold only in some data. |
Rui Kang; Shaoxu Song; Chaokun Wang; |
| 226 | Improving Prediction-Based Lossy Compression Dramatically Via Ratio-Quality Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between reconstructed data quality and compression ratio is prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on the prediction-based lossy compression framework, which can effectively foresee the reduced data quality and compression ratio, as well as the impact of lossy compressed data on post-hoc analysis quality. |
Sian Jin; Sheng Di; Jiannan Tian; Suren Byna; Dingwen Tao; Franck Cappello; |
| 227 | LAN: Learning-based Approximate K-Nearest Neighbor Search in Graph Databases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the approximate k-nearest neighbor (k-ANN) search with the aim of trading efficiency with a slight decrease in accuracy. |
Yun Peng; Byron Choi; Tsz Nam Chan; Jianliang Xu; |
| 228 | Exploiting Hierarchical Parallelism and Reusability in Tensor Kernel Processing on Heterogeneous HPC Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Canonical Polyadic Decomposition (CPD) of sparse tensors is an effective tool in various machine learning and data analytics applications, in which sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the major performance bottleneck. To overcome this bottleneck and support efficient applications, this paper presents HPSpTM, an efficient sparse MTTKRP framework, to exploit the multi-level parallelism and reusability on heterogeneous HPC systems. |
Yuedan Chen; Guoqing Xiao; M. Tamer Özsu; Zhuo Tang; Albert Y. Zomaya; Kenli Li; |
| 229 | Consistent Subgraph Matching Over Large Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study fundamental problems for CGDs and CSM. |
Ye Yuan; Delong Ma; Aoqian Zhang; Guoren Wang; |
| 230 | PinSQL: Pinpoint Root Cause SQLs to Resolve Performance Issues in Cloud Databases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the presence of a large number of queries, to pinpoint the R-SQLs is far more difficult than to identify the H-SQLs. To address this challenge, we aim at automatically pinpointing the R-SQLs to resolve performance issues in cloud databases. |
Xiaoze Liu; Zheng Yin; Chao Zhao; Congcong Ge; Lu Chen; Yunjun Gao; Dimeng Li; Ziting Wang; Gaozhong Liang; Jian Tan; Feifei Li; |
| 231 | Dynamic Model Tree for Interpretable Data Stream Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we revisit Model Trees for machine learning in evolving data streams. |
Johannes Haug; Klaus Broelemann; Gjergji Kasneci; |
| 232 | FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new FL protocol termed FedADMM based on primal-dual optimization. |
Yonghai Gong; Yichuan Li; Nikolaos M. Freris; |
| 233 | Colorful H-star Core Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a relaxed higher-order cohesive subgraph model, called colorful h-star core, based on counting the number of colorful h-stars. |
Sen Gao; Rong-Hua Li; Hongchao Qin; Hongzhi Chen; Ye Yuan; Guoren Wang; |
| 234 | Mixing Transactions with Arbitrary Values on Blockchains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formally define the anonymity-aware output decomposition (AA-OD) problem, which aims to find a c-decomposition with a minimum number of decomposed outputs for a given original output set. |
Wangze Ni; Peng Cheng; Lei Chen; |
| 235 | TSPLIT: Fine-grained GPU Memory Management for Efficient DNN Training Via Tensor Splitting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose TSPLIT, a fine-grained DNN memory management system that breaks apart memory bottlenecks while maintaining the efficiency of DNNs training. |
Xiaonan Nie; Xupeng Miao; Zhi Yang; Bin Cui; |
| 236 | Out-of-Core Edge Partitioning at Linear Run-Time Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose 2PS-L, a novel out-of-core edge partitioning algorithm that builds upon the stateful streaming model, but achieves linear run-time i.e.,O(|E|)). |
Ruben Mayer; Kamil Orujzade; Hans-Arno Jacobsen; |
| 237 | FedRecAttack: Model Poisoning Attack to Federated Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. |
Dazhong Rong; Shuai Ye; Ruoyan Zhao; Hon Ning Yuen; Jianhai Chen; Qinming He; |
| 238 | Scalable Motif Counting for Large-scale Temporal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a scalable parallel framework for exactly counting temporal motifs in large-scale temporal graphs. |
Zhongqiang Gao; Chuanqi Cheng; Yanwei Yu; Lei Cao; Chao Huang; Junyu Dong; |
| 239 | SPADE: GPU-Powered Spatial Database Engine for Commodity Hardware Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present SPADE, a GPU-powered spatial database engine that supports a rich set of spatial queries. |
Harish Doraiswamy; Juliana Freire; |
| 240 | GX-Plug: A Middleware for Plugging Accelerators to Distributed Graph Processing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a middleware, called the GX-plug, for the ease of integrating the merits of both. |
Kai Zou; Xike Xie; Qi Li; Deyu Kong; |
| 241 | Exploration of Knowledge Graphs Via Online Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We thus devise an algorithm for online aggregation that specializes in exploration queries on knowledge graphs; our proposal leverages the low dimension of RDF graphs, and the low selectivity of exploration queries, by augmenting random walks with exact partial computations using a worst-case optimal join algorithm. |
Oren Kalinsky; Aidan Hogan; Oren Mishali; Yoav Etsion; Benny Kimelfeld; |
| 242 | Effective Explanations for Entity Resolution Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the fundamental problem of explainability of the DL solution for ER. |
Tommaso Teofili; Donatella Firmani; Nick Koudas; Vincenzo Martello; Paolo Merialdo; Divesh Srivastava; |
| 243 | Factor Windows: Cost-based Query Rewriting for Optimizing Correlated Window Aggregates Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In Azure Stream Analytics (ASA), a stream processing service hosted by Microsoft’s Azure cloud, we see many customer queries that contain aggregate functions (such as MIN and MAX) over multiple correlated windows (e.g., tumbling windows of length five minutes and ten minutes) defined on the same event stream. In this paper, we present a cost-based optimization framework for optimizing such queries by sharing computation among multiple windows. |
Wentao Wu; Philip A. Bernstein; Alex Raizman; Christina Pavlopoulou; |
| 244 | SPES: A Symbolic Approach to Proving Query Equivalence Under Bag Semantics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In database-as-a-service platforms, automated ver-ification of query equivalence helps eliminate redundant computation in the form of overlapping sub-queries. Researchers have proposed two pragmatic techniques to tackle this problem. |
Qi Zhou; Joy Arulraj; Shamkant B. Navathe; William Harris; Jinpeng Wu; |
| 245 | Manipulating Structural Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Manipulation of SCAN can be used to assess its robustness and lay the groundwork for developing robust clustering algorithms. To fill this gap and considering the importance of the $\epsilon$-neighborhood for SCAN, we propose a problem, denoted as MN, for manipulating SCAN. |
Wentao Li; Min Gao; Dong Wen; Hongwei Zhou; Cai Ke; Lu Qin; |
| 246 | RobustScaler: QoS-Aware Autoscaling for Complex Workloads Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present RobustScaler to achieve superior trade-off between cost and QoS. |
Huajie Qian; Qingsong Wen; Liang Sun; Jing Gu; Qiulin Niu; Zhimin Tang; |
| 247 | Ranked Window Query Retrieval Over Video Repositories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we initiate the study of ranked window queries that aim to retrieve clips from large video repositories in which objects co-occur in a query-specified fashion. |
Yueting Chen; Xiaohui Yu; Nick Koudas; |
| 248 | Horae: A Graph Stream Summarization Structure for Efficient Temporal Range Query Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Horae, a novel graph stream summarization structure for efficient temporal range query, which presents a time prefix embedded multi-layer summarization structure. |
Ming Chen; Renxiang Zhou; Hanhua Chen; Jiang Xiao; Hai Jin; Bo Li; |
| 249 | Local Clustering Over Labeled Graphs: An Index-Free Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study local clustering over labeled graphs, which extracts a subgraph with nodes having high label density matched to the query labels as well as high structure density around a seed node. |
Niu Yudong; Yuchen Li; Ju Fan; Zhifeng Bao; |
| 250 | Adaptive Partitioning for Large-Scale Graph Analytics in Geo-Distributed Data Centers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose RLCut, which uses Reinforcement Learning (RL) to help taming the complexity of the problem. |
Amelie Chi Zhou; Juanyun Luo; Ruibo Qiu; Haobin Tan; Bingsheng He; Rui Mao; |
| 251 | Index-based Structural Clustering on Directed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, SCAN assumes that the input graph is undirected and can not cluster the directed graphs. To address this problem, in this paper, we propose a new structural clustering model based on SCAN to cluster directed graphs. |
Lingkai Meng; Long Yuan; Zi Chen; Xuemin Lin; Shiyu Yang; |
| 252 | Epidemic Spread Optimization for Disease Containment with NPIs and Vaccination Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of Containment Operation Optimization Design (COOD) that optimizes the epidemic containment by carefully analyzing contacts between individuals. |
Ya-Wen Teng; Yishuo Shi; De-Nian Yang; Wang-Chien Lee; Philip S. Yu; Ying-Liang Lu; Ming-Syan Chen; |
| 253 | PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The last is the support of HTAP to eliminate data redundancy and system complexity from heterogeneous databases. To cater to these trends, we design a distributed relational database called PolarDB-X, which is built on top of the cloud-native database PolarDB. |
Wei Cao; Feifei Li; Gui Huang; Jianghang Lou; Jianwei Zhao; Dengcheng He; Mengshi Sun; Yingqiang Zhang; Sheng Wang; Xueqiang Wu; Han Liao; Zilin Chen; Xiaojian Fang; Mo Chen; Chenghui Liang; Yanxin Luo; Huanming Wang; Songlei Wang; Zhanfeng Ma; Xinjun Yang; Xiang Peng; Yubin Ruan; Yuhui Wang; Jie Zhou; Jianying Wang; Qingda Hu; Junbin Kang; |
| 254 | Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To contend with the limitations of existing solutions, we propose a Weakly-Supervised Contrastive learning model. |
Sean Bin Yang; Chenjuan Guo; Jilin Hu; Bin Yang; Jian Tang; Christian S. Jensen; |
| 255 | Micro-Behavior Encoding for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to investigate the effects of the micro-behavior information in SR systematically. |
Jiahao Yuan; Wendi Ji; Dell Zhang; Jinwei Pan; Xiaoling Wang; |
| 256 | Towards Spatio- Temporal Aware Traffic Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a framework that aims at turning spatio-temporal agnostic models to spatio-temporal aware models. |
Razvan-Gabriel Cirstea; Bin Yang; Chenjuan Guo; Tung Kieu; Shirui Pan; |
| 257 | Aggregate Queries on Knowledge Graphs: Fast Approximation with Semantic-aware Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a “sampling-estimation” model to answer aggregate queries over KGs, which is the first work to provide an approximate aggregate result with an effective accuracy guarantee, and without relying on factoid queries. |
Yuxiang Wang; Arijit Khan; Xiaoliang Xu; Jiahui Jin; Qifan Hong; Tao Fu; |
| 258 | A Sketch-based Index for Correlated Dataset Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a specific type of data-driven query that supports relational data augmentation through numerical data relationships: given an input query table, find the top-k tables that are both joinable with it and contain columns that are correlated with a column in the query. |
Aécio Santos; Aline Bessa; Christopher Musco; Juliana Freire; |
| 259 | Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by the idea of meta-augmentation, in this paper, by treating a user’s preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues. |
Yan Zhang; Changyu Li; Ivor W. Tsang; Hui Xu; Lixin Duan; Hongzhi Yin; Wen Li; Jie Shao; |
| 260 | ExSample: Efficient Searches on Video Repositories Through Adaptive Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ExSample, a low cost framework for object search over un-indexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed. |
Oscar Moll; Favyen Bastani; Sam Madden; Mike Stonebraker; Vijay Gadepally; Tim Kraska; |
| 261 | Fast Adaptive Similarity Search Through Variance-Aware Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Similarly, hardware-accelerated quantization methods may sacrifice accuracy to speed up the query execution. We propose a Variance-Aware Quantization (VAQ) method to encode data by intelligently adapting dictionary sizes to subspaces to alleviate these significant drawbacks. |
John Paparrizos; Ikraduya Edian; Chunwei Liu; Aaron J. Elmore; Michael J. Franklin; |
| 262 | Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Self-Supervised Hypergraph Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. |
Zhonghang Li; Chao Huang; Lianghao Xia; Yong Xu; Jian Pei; |
| 263 | Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases that avoids the large throughput losses of traditional HTAP systems. |
Amirali Boroumand; Saugata Ghose; Geraldo F. Oliveira; Onur Mutlu; |
| 264 | BA-GNN: On Learning Bias-Aware Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on how to address the bias issue on graphs and learn a graph neural network model that is robust to arbitrary unknown distribution shifts. |
Zhengyu Chen; Teng Xiao; Kun Kuang; |
| 265 | Constructing Compact Time Series Index for Efficient Window Query Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a compact time series index (WinIdx) for efficient window query processing. |
Jing Zhao; Peng Wang; Bo Tang; Lu Liu; Chen Wang; Wei Wang; Jianmin Wang; |
| 266 | Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised auto encoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. |
Tung Kieu; Bin Yang; Chenjuan Guo; Christian S. Jensen; Yan Zhao; Feiteng Huang; Kai Zheng; |
| 267 | Prediction Intervals for Learned Cardinality Estimation: An Experimental Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a systematic investigation of potential approaches for obtaining prediction intervals. |
Saravanan Thirumuruganathan; Suraj Shetiya; Nick Koudas; Gautam Das; |
| 268 | ExSample: Efficient Searches on Video Repositories Through Adaptive Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ExSample, a low cost framework for object search over un-indexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed. |
Oscar Moll; Favyen Bastani; Sam Madden; Mike Stonebraker; Vijay Gadepally; Tim Kraska; |
| 269 | Pixels: An Efficient Column Store for Cloud Data Lakes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Pixels, a column store optimized for the cloud that solves the problem by (1) the workload-driven storage layout optimization within and across the row group boundaries; (2) the I/O scheduling concerning the optimized storage layout and the performance characteristics of COS. |
Haoqiong Bian; Anastasia Ailamaki; |
| 270 | Time- and Space-Efficient Regular Path Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a time- and space-efficient technique to solve regular path queries over labeled (RDF) graphs. |
Diego Arroyuelo; Aidan Hogan; Gonzalo Navarro; Javiel Rojas-Ledesma; |
| 271 | Subgraph Query Generation with Fairness and Diversity Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We formalize a bi-criteria optimization problem that aims to find a Pareto optimal set of query instances in terms of diversity and fairness measures. |
Hanchao Ma; Sheng Guan; Mengying Wang; Yen-shuo Chang; Yinghui Wu; |
| 272 | Reducing Write Amplification of LSM-Tree with Block-Grained Compaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They will lower both write and read performance of the LSM-tree. To address these issues, we propose a novel compaction scheme named Block Compaction that adopts a block-grained merging policy to perform compaction operations on the LSM-tree. |
Xiaoliang Wang; Peiquan Jin; Bei Hua; Hai Long; Wei Huang; |
| 273 | Learning Graph Convolutional Networks Based on Quantum Vertex Information Propagation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. |
Lu Bai; Yuhang Jiao; Lixin Cui; Luca Rossi; Yue Wang; Philip S. Yu; Edwin R. Hancock; |
| 274 | CheetahKG: A Demonstration for Core-based Top-$k$ Frequent Pattern Discovery on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top-$k$ frequent pattern discovery. |
Bo Tang; Jian Zeng; Qiandong Tang; Chuan Yang; Qiaomu Shen; Leong Hou U; Xiao Yan; Dan Zeng; |
| 275 | How Learning Can Help Complex Cyclic Join Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the existing work, such decomposition is done by estimation with AGM bound. In this work, we demonstrate how ML/DL can support such complex cyclic self-joins by providing a more accurate estimation. |
Hao Zhang; Qiyan Li; Kangfei Zhao; Jeffrey Xu Yu; Yuanyuan Zhu; |
| 276 | VC-Tune: Tuning and Exploring Distributed Vertex-Centric Graph Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To ad-dress the important challenge, we present V C- Tune, a system with a convenient interface to help practitioners orchestrate the unit tasks for improving the overall performance within the system limit. |
Zichen Zhu; Siqiang Luo; Xiaokui Xiao; Yin Yang; Dingheng Mo; Yufei Han; |
| 277 | A Demonstration of RASED: A Scalable Dashboard for Monitoring Road Network Updates in OSM Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Road network queries (e.g., shortest path, range, and k-NN) hinge on the road network quality, which, un-fortunately, suffer from all sorts of inaccuracy. Given that OpenStreetMap … |
Mashaal Musleh; Mohamed F. Mokbel; |
| 278 | VICS-GNN: A Visual Interactive System for Community Search Via Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate VICS-GNN, a Visual Interactive system for Community Search via graph Neural Network. |
Jiazun Chen; Jun Gao; |
| 279 | Samba: A System for Secure Federated Multi-Armed Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate Samba, a generic framework for Secure federAted Multi-armed BAndits. |
Gael Marcadet; Radu Ciucanu; Pascal Lafourcade; Marta Soare; Sihem Amer-Yahia; |
| 280 | A Knowledge Base Question Answering System for Cyber Threat Knowledge Acquisition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap, we propose ThreatQA, a system that facilitates cyber threat knowledge acquisition via knowledge base question answering. |
Zhengjie Ji; Edward Choi; Peng Gao; |
| 281 | AbcOD: Mining Band Order Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the design of and a demonstration plan for abcOD, a tool for efficiently discovering approximate band conditional order dependencies (abcODs) from data. |
Pei Li; Jessica Jessica; Naida Tania; Michael Böhlen; Divesh Srivastava; Jaroslaw Szlichta; |
| 282 | SEFrame: An SGX-enhanced Smart Contract Execution Framework for Permissioned Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We use real-time dashboards containing the output of SEFrame, which allows attendees to interactively explore how SEFrame achieves efficient inter- and intra-node concurrency. |
Min Fang; Xinna Zhou; Zhao Zhang; Cheqing Jin; Aoying Zhou; |
| 283 | NeoDBMS: In-situ Snapshots for Multi-Version DBMS on Native Computational Storage Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we demonstrate how neoDBMS performs snapshot computation in-situ. |
Arthur Bernhardt; Sajjad Tamimi; Tobias Vinçon; Christian Knoedler; Florian Stock; Carsten Heinz; Andreas Koch; Ilia Petrov; |
| 284 | ExRumourLens: Auditable Rumour Detection with Multi-View Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing alerting mechanisms are limited to post-hoc classification, and rumours are often detected after the damage has been done. This paper presents exRumourLens, a system that enables tracking and auditing of potential rumours as they emerge. |
Thanh Cong Phan; Thanh Tam Nguyen; Matthias Weidlich; Hongzhi Yin; Jun Jo; Quoc Viet Hung Nguyen; |
| 285 | How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine–Grained Provenance Analysis for SQL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate the prove-nance analysis of SQL queries and use it for query debugging. |
Tobias Müller; Pascal Engel; |
| 286 | IKAROS: An Indoor Keyword-Aware Routing System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate an Indoor Keyword-Aware Routing System (IKAROS) which efficiently answers the indoor top-$k$ keyword-aware routing query (IKRQ). |
Tiantian Liu; Zijin Feng; Huan Li; Hua Lu; Lidan Shou; Jianliang Xu; |
| 287 | Farming Your ML-based Query Optimizer’s Food Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, collecting such a training dataset is a very tedious and time-consuming task: It requires both developing numerous jobs and executing them to acquire ground-truth labels. We demonstrate Datafarm,a novel framework for efficiently generating and labeling training data for ML-based query optimizers to overcome these issues. |
Robin Van De Water; Francesco Ventura; Zoi Kaoudi; Jorge-Arnulfo Quiané-Ruiz; Volker Markl; |
| 288 | Efficient GPU-accelerated Join Optimization for Complex Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing optimal solutions either cannot be effectively parallelized, or are inefficient while doing a lot of unnecessary work. In this demonstration, we present our system, GPU-QO, which aims to demonstrate query optimization techniques for large analytical queries using GPUs. |
Vasilis Mageirakos; Riccardo Mancini; Srinivas Karthik; Bikash Chandra; Anastasia Ailamaki; |
| 289 | Automatic Performance Tuning for Distributed Data Stream Processing Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This tutorial offers a comprehensive review of the state-of-the-art automatic performance tuning approaches that have been proposed in recent years. |
Herodotos Herodotou; Lambros Odysseos; Yuxing Chen; Jiaheng Lu; |
| 290 | Machine Learning for Data Management: A System View Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Third, with various data management requirements, how to validate whether the machine learning models can meet the requirements? In this tutorial, we discuss existing learning-based data management studies and how they solve the above challenges, and provide some future research directions. |
Guoliang Li; Xuanhe Zhou; |
| 291 | Time Series Anomaly Detection Toolkit for Data Scientist Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This tutorial presents a design and implementation of a scikit-compatible system for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning. |
Dhaval Patel; Dzung Phan; Markus Mueller; |
| 292 | Toward Responsive DBMS: Optimal Join Algorithms, Enumeration, Factorization, Ranking, and Dynamic Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Arguably, avoiding query plans that produce huge intermediate results has been an overarching goal of database optimizers, which is part of the reason why optimal join algorithms, enumeration, and factorized representations have generated a lot of excitement. In this tutorial, we embark on an exploration of these topics, showing how they are intimately connected with a wide range of fundamental problems in computer science. |
Nikolaos Tziavelis; Wolfgang Gatterbauer; Mirek Riedewald; |
| 293 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A host of technical advances have been made and several explanation methods have been proposed in recent years that address the problem of model explainability. In this tutorial, we will present these novel explanation approaches, characterize their strengths and limitations, and enumerate opportunities for data management research in the context of XAI. |
Romila Pradhan; Aditya Lahiri; Sainyam Galhotra; Babak Salimi; |
| 294 | Database Optimizers in The Era of Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this tutorial, we review advances made recently in a decades-old problem, namely query optimization. |
Dimitris Tsesmelis; Alkis Simitsis; |
| 295 | Analytics at Scale: Evolution at Infrastructure and Algorithmic Levels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this tutorial, we discuss the advances and innovations introduced at both the infrastructure and algorithmic levels, directed at making analytic workloads scale, while paying close attention to the kind of quality of service guarantees different technology provide. |
Mohammed Al-Kateb; Mohamed Y. Eltabakh; Awny Al-Omari; Paul G. Brown; |
| 296 | How Much Storage Do We Need for High Performance Server Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The ability to support AI applications has become a key evaluation metric for the performance of HPS. Therefore, we propose an HPS storage design solution for typical AI applications. |
Jia Wei; Xingjun Zhang; |
| 297 | Constructing A Skeleton Database and Enriching It Using A Generative Adversarial Network (GAN) Simulator to Assess Human Movement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose enriching a database that contains a limited number of videos of human physiotherapy exercises by generating synthetic data. |
Yoram Segal; Ofer Hadar; |
| 298 | Novel Methods for Aggregating Analyst Estimates Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a systematic review of academic literature relating to the study of analyst estimates. |
Kheng Kua; |
| 299 | A Journey from Commit Processing in Distributed Databases to Consensus in Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a thorough analysis of the various blockchain consensus algorithms is conducted including their benefits and drawbacks. |
Kamal Kant; Sarvesh Pandey; Udai Shanker; |
| 300 | Discovering Actual Delivery Locations from Mis-Annotated Couriers’ Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to infer actual delivery locations of addresses from couriers’ trajectories. |
Sijie Ruan; Cheng Long; Xiaodu Yang; Tianfu He; Ruiyuan Li; Jie Bao; Yiheng Chen; Shengnan Wu; Jiangtao Cui; Yu Zheng; |
| 301 | Intent Mining: A Social and Semantic Enhanced Topic Model for Operation-Friendly Digital Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the digital marketing where marketing officers (MOs) have to commit to creating brand new promotion ads/contents based on understandings of users’ needs or preferences. |
Weifan Wang; Xiaocheng Cheng; Ziqi Liu; Yu Lin; Yue Shen; Binbin Hu; Zhiqiang Zhang; Xiaodong Zeng; Jun Zhou; Jinjie Gu; Minnan Luo; |
| 302 | Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework, the Tower Bridge Net (TB-Net), using the proposed bidirectional embedding propagation approach to achieve both superior recommendation and explainability performances. |
Shendi Wang; Haoyang Li; Caleb Chen Cao; Xiao-Hui Li; Ng Ngai Fai; Jianxin Liu; Xun Xue; Hu Song; Jinyu Li; Guangye Gu; Lei Chen; |
| 303 | Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a self-supervised and user behavior-oriented product taxonomy expansion framework to append new concepts into existing taxonomies. |
Sijie Cheng; Zhouhong Gu; Bang Liu; Rui Xie; Wei Wu; Yanghua Xiao; |
| 304 | Detecting Loaded Trajectories for Hazardous Chemicals Transportation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the challenges, we propose a LoadEd trAjectory Detectlon framework, called LEAD, to detect the loaded trajectory from the raw HCT trajectory accurately and efficiently. |
Shuncheng Liu; Zhi Xu; Huimin Ren; Tianfu He; Boyang Han; Jie Bao; Kai Zheng; Yu Zheng; |
| 305 | Multi-Task Learning with Calibrated Mixture of Insightful Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, an in-depth empirical investigation into negative transfer is launched. |
Sinan Wang; Yumeng Li; Hongyan Li; Tanchao Zhu; Zhao Li; Wenwu Ou; |
| 306 | Gaia: Graph Neural Network with Temporal Shift Aware Attention for Gross Merchandise Value Forecast in E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we present a solution to better forecast GMV inside Alipay app. |
Borui Ye; Shuo Yang; Binbin Hu; Zhiqiang Zhang; Youqiang He; Kai Huang; Jun Zhou; Yanming Fang; |
| 307 | Adaptive Task Planning for Large-Scale Robotized Warehouses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. |
Dingyuan Shi; Yongxin Tong; Zimu Zhou; Ke Xu; Wenzhe Tan; Hongbo Li; |
| 308 | Separation or Not: On Handing Out-of-Order Time-Series Data in Leveled LSM-Tree Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Note that as an industrial paper, rather than proposing novel techniques for research problems, we focus on the practice of whether separating or not for lower write amplification. |
Yuyuan Kang; Xiangdong Huang; Shaoxu Song; Lingzhe Zhang; Jialin Qiao; Chen Wang; Jianmin Wang; Julian Feinauer; |
| 309 | APOTS: A Model for Adversarial Prediction of Traffic Speed Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel model, named as adversarial prediction of traffic speed (APOTS), based on adversarial training, data augmentation, and hybrid deep learning modeling. |
Namhyuk Kim; Junho Song; Siyoung Lee; Jaewon Choe; Kyungsik Han; Sunghwan Park; Sang-Wook Kim; |
| 310 | Feature Augmentation with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We implement two algorithms, one with multi-arm bandit and the other with branch Deep Q Networks (branch DQN), to realize the framework of AutoFeature. |
Jiabin Liu; Chengliang Chai; Yuyu Luo; Yin Lou; Jianhua Feng; Nan Tang; |
| 311 | Rule Learning Over Knowledge Graphs with Genetic Logic Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend the rule hypothesis space from usual path rules to general Datalog rule space by proposing a novel Genetic Logic Programming algorithm named Evoda. |
Lianlong Wu; Emanuel Sallinger; Evgeny Sherkhonov; Sahar Vahdati; Georg Gottlob; |
| 312 | AiRS: A Large-Scale Recommender System at NAVER News Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a large-scale news recommender system named as AiRS, present how it jointly leverages the four DCs for NAVER News service. |
Hongjun Lim; Yeon-Chang Lee; Jin-Seo Lee; Sanggyu Han; Seunghyeon Kim; Yeongjong Jeong; Changbong Kim; Jaehun Kim; Sunghoon Han; Solbi Choi; Hanjong Ko; Dokyeong Lee; Jaeho Choi; Yungi Kim; Hong-Kyun Bae; Taeho Kim; Jeewon Ahn; Hyun-Soung You; Sang-Wook Kim; |
| 313 | Cheaper Is Better: Exploring Price Competitiveness for Online Purchase Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Without carefully considering these comparison prices, methods fail to capture the purchase motivation attributable to prices comprehensively. To address this problem, in this paper, we introduce the concept of item price competitiveness. |
Han Wu; Hongzhe Zhang; Liangyue Li; Zulong Chen; Fanwei Zhu; Xiao Fang; |
| 314 | Field-aware Variational Autoencoders for Billion-scale User Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we harness the unsupervised advantages of Variational Autoencoders (VAEs), to learn user representation from large-scale, high-dimensional, and multi-field data. |
Ge Fan; Chaoyun Zhang; Junyang Chen; Baopu Li; Zenglin Xu; Yingjie Li; Luyu Peng; Zhiguo Gong; |
| 315 | Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. |
Jiayuan Ding; Tong Xiang; Zijing Ou; Wangyang Zuo; Ruihui Zhao; Chenhua Lin; Yefeng Zheng; Bang Liu; |
| 316 | AMCAD: Adaptive Mixed-Curvature Representation Based Advertisement Retrieval System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods either only consider a single geometry space, or combine several spaces manually, which are incapable and inflexible to model the complexity and heterogeneity in the real scenario. To tackle this challenge, we present a web-scale Adaptive Mixed-Curvature ADvertisement retrieval system (AM-CAD) to automatically capture the complex and heterogeneous graph structures in non-Euclidean spaces. |
Zhirong Xu; Shiyang Wen; Junshan Wang; Guojun Liu; Liang Wang; Zhi Yang; Lei Ding; Yan Zhang; Di Zhang; Jian Xu; Bo Zheng; |
| 317 | PICASSO: Unleashing The Potential of GPU-centric Training for Wide-and-deep Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This issue can be explained by two characteristics of these recommendation models: First, they contain up to a thousand of input feature fields, introducing fragmentary and memory-intensive operations; Second, the multiple constituent feature interaction submodules introduce substantial small-sized compute kernels. To remove this roadblock to the development of recommender systems, we propose a novel framework named PICASSO to accelerate the training of recommendation models on commodity hardware. |
Yuanxing Zhang; Langshi Chen; Siran Yang; Man Yuan; Huimin Yi; Jie Zhang; Jiamang Wang; Jianbo Dong; Yunlong Xu; Yue Song; Yong Li; Di Zhang; Wei Lin; Lin Qu; Bo Zheng; |
| 318 | Knowledge Enhanced Person-Job Fit for Talent Recruitment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is commonly known that there exists a semantic gap between textual job postings and textual resumes. Therefore, in this paper, we study how to improve person-job fit by bridging this semantic gap with the help of prior knowledge. |
Kaichun Yao; Jingshuai Zhang; Chuan Qin; Peng Wang; Hengshu Zhu; Hui Xiong; |
| 319 | ODNET: A Novel Personalized Origin-Destination Ranking Network for Flight Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel personalized Origin-Destination ranking NETwork (ODNET) for flight recommendation. |
Jia Xu; Jin Huang; Zulong Chen; Yang Li; Wanjie Tao; Chuanfei Xu; |
| 320 | FAIR-DB: A System to Discover Unfairness in Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this context, it is crucial to ensure that the data on which we base these decisions, are fair, and do not reflect historical biases. In this demo, we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a system that exploiting the notion of Functional Dependency, a particular type of constraint on the data, can discover unethical behaviours in a dataset. |
Fabio Azzalini; Chiara Criscuolo; Letizia Tanca; |