2023 IEEE 39th International Conference on Data Engineering (ICDE)最新文献

{"title":"A Survey on Knowledge Graph-Based Recommender Systems : Extended Abstract","authors":"Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He","doi":"10.1109/ICDE55515.2023.00328","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00328","url":null,"abstract":"To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129487626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Double Hierarchical Labeling Shortest Distance Querying in Time-dependent Road Networks","authors":"Tangpeng Dan, Xiao Pan, Bolong Zheng, Xiaofeng Meng","doi":"10.1109/ICDE55515.2023.00161","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00161","url":null,"abstract":"A shortest distance query is a fundamental operation of various real-time response applications in time-dependent road networks. Unfortunately, existing methods (e.g., G-treelike, 2-hop labeling-like) are prohibitively expensive in terms of space/time. To this end, we propose a novel Double Hierarchical Labeling (DHL) index, which consists of a Hierarchical Graph Partition (HGP) tree and a hierarchical border labeling list. For HGP-tree, we first use a hierarchical graph partitioning to split the entire road network into hierarchical subgraphs and then index these subgraphs by a balanced tree. To preserve all connectivity information between border vertices of subgraphs, a Time-based Distance Inverted File (TDIF) is constructed for each leaf node of the HGP-tree. For the hierarchical labeling list, we construct it only for border vertices and use it to speed up query processing. Moreover, a label propagation update is proposed to manage label updating when weights change. Finally, we propose a phase-aware search algorithm for different search situations between given query vertices to guarantee query efficiency. Extensive experiments are conducted to demonstrate the superiority of the proposed proposals on query processing and index maintenance.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131285049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ziziphus: Scalable Data Management Across Byzantine Edge Servers","authors":"Mohammad Javad Amiri, Daniel H. Shu, Sujaya Maiyya, D. Agrawal, A. E. Abbadi","doi":"10.1109/ICDE55515.2023.00044","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00044","url":null,"abstract":"Edge computing while bringing computation and data closer to users in order to improve response time, distributes edge servers in wide area networks resulting in increased communication latency between the servers. Synchronizing globally distributed edge servers, especially in the presence of Byzantine servers, becomes costly due to the high communication complexity of Byzantine fault-tolerant consensus protocols. In this paper, we present Ziziphus, a geo-distributed system that partitions edge servers into fault-tolerant zones where each zone processes transactions initiated by nearby clients locally. Global synchronization among zones is required only in special situations, e.g., migration of clients from one zone to another. On the one hand, the two-level architecture of Ziziphus confines the malicious behavior of nodes within zones requiring a much cheaper protocol at the top level for global synchronization. On the other hand, Ziziphus processes local transactions within zones by edge servers closer to clients resulting in enhanced performance. Ziziphus further introduces zone clusters to enhance scalability where instead of running global synchronization among all zones, only zones of a single cluster are synchronized.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121611573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iterative Refinement for Multi-Source Visual Domain Adaptation (Extended abstract)","authors":"Hanrui Wu, Yuguang Yan, Guosheng Lin, Min Yang, Michael K. Ng, Qingyao Wu","doi":"10.1109/ICDE55515.2023.00341","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00341","url":null,"abstract":"Multi-source domain adaptation (MSDA) aims to leverage the knowledge in multiple source domains to assist the prediction in a target domain, where the source and target domains have different data distributions. This paper presents a MSDA model to investigate both domain discrepancy and domain relevance, whose interactions are also exploited to gradually refine the learning performance. Particularly, the proposed model contains two components, i.e., feature spaces learning and transferred weights learning. The former one minimizes the domain discrepancy and the latter one evaluates the domain relevance. Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116391005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active Sampling for Sparse Table by Bayesian Optimization with Adaptive Resolution","authors":"Xiao He, Jian Tan, Bin Wu, Feifei Li, Xinping Zhang, Gaozhong Liang, Jinfeng Xu","doi":"10.1109/ICDE55515.2023.00068","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00068","url":null,"abstract":"Open-source relational database systems have become increasingly popular in the cloud era. However, practitioners are often beset with query performance issues. Thus a general-purpose database performance tuning tool independent of the various DBMS kernels becomes desired to lower the bar of using these systems. The first mandatory step in developing such a tool is to design an effective sampling method that collects representative records from different tables. Although one could leverage standard SQL statements and indexes to achieve this, sampling performance and statistical efficiency are not guaranteed when the underlying tables are frequently updated, especially for Sparse Tables where the range of index values is significantly greater than the table size.To this end, we propose a novel Active Sampling algorithm that queries regions more likely to contain data records from Sparse Tables. It relies on Gaussian process regression to characterize the probability density of whether a data record is non-null at a given index value. With the help of this estimated density function, the proposed method achieves efficient sampling by actively querying records with adaptive resolutions of interval lengths and provides an unbiased estimator for histogram construction. Comprehensive experiments on synthetic and real-world datasets demonstrate that the proposed Active Sampling method can effectively improve the estimation accuracy and use less query cost than other commonly used sampling methods.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126069611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact-aware Maneuver Decision with Enhanced Perception for Autonomous Vehicle","authors":"Shuncheng Liu, Yuyang Xia, Xu Chen, Jiandong Xie, Han Su, Kaiyu Zheng","doi":"10.1109/ICDE55515.2023.00250","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00250","url":null,"abstract":"Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r.t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"38 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126598854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SK-Gradient: Efficient Communication for Distributed Machine Learning with Data Sketch","authors":"Jie Gui, Yuchen Song, Zezhou Wang, Chenhong He, Qun Huang","doi":"10.1109/ICDE55515.2023.00183","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00183","url":null,"abstract":"With the explosive growth of data volume, distributed machine learning has become the mainstream approach for training deep neural networks. However, distributed machine learning incurs non-trivial communication overhead. To this end, various compression schemes are proposed to alleviate the communication volume among nodes. Nevertheless, existing compression schemes, such as gradient quantization or gradient sparsification, suffer from low compression ratios and/or high computational overheads. Recent studies advocate leveraging sketch techniques to assist these schemes. However, the limitations of gradient quantization and gradient sparsification remain. In this paper, we propose SK-Gradient, a novel gradient compression scheme that solely builds on sketch. The core component of SK-Gradient is a novel sketch namely FGC Sketch that is tailored for gradient compression. FGC Sketch precomputes the costly hash functions to alleviate computational overheads. Its simplified design makes it convenient for GPU acceleration. In addition, SK-Gradient leverages various techniques including selective gradient compression and periodic synchronization strategy to improve computational efficiency and compression accuracy. Compared with the state-of-the-art schemes, SK-Gradient achieves up to 92.9% reduction in computational overhead and up to 95.2% improvement in training speedups at the same compression ratio.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Node-Similarity Distribution of Complex Networks and Its Applications in Link Prediction (Extended Abstract)","authors":"Cunlai Pu, Jie Li, Jian Wang, Tony Q. S. Quek","doi":"10.1109/ICDE55515.2023.00376","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00376","url":null,"abstract":"Node-similarity distributions not only characterize different types of complex networks, but also offer insights in the structural predictability of complex networks, and even facilitate prediction tasks in complex networks. By means of the generating function, we propose a framework to calculate the common neighbor based similarity (CNS) distributions, offering theoretical results of similarity distributions of various complex networks. Furthermore, we apply node-similarity distributions to link prediction, a key task in network analysis. Specifically, by deriving analytical solutions for two metrics: i) precision and ii) area under the receiver operating characteristic curve (AUC), we give theoretical evaluation of link prediction. Also, by analyzing i) the expected prediction accuracy of similarity scores and ii) optimal prediction priority of unconnected node pairs, we optimize link prediction with similarity distributions. Simulation results confirm our findings and also validate the proposed methods for evaluating and optimizing link prediction.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128804732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEIGN: A Simple and Efficient Graph Neural Network for Large Dynamic Graphs","authors":"Xiao Qin, Nasrullah Sheikh, Chuan Lei, B. Reinwald, Giacomo Domeniconi","doi":"10.1109/ICDE55515.2023.00218","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00218","url":null,"abstract":"Graph neural networks (GNNs) have accomplished great success in learning complex systems of relations arising in broad problem settings ranging from e-commerce, social networks to data management. Training GNNs over large-scale graphs poses challenges for constrained compute resources due to the heavy data dependencies between the nodes. Moreover, modern relational data is constantly evolving, which creates an additional layer of learning challenges with respect to the model scalability and expressivity. This paper introduces a simple and efficient learning algorithm for large discrete-time dynamic graphs (DTDGs) – a widely adopted data model for many applications. We particularly tackle two critical challenges: (1) how the model can be efficiently trained on large-scale DTDGs to exploit hardware accelerators with small memory footprint, and (2) how the model can effectively capture the changing dynamics of the graphs. To the best of our knowledge, existing GNNs fail to address both challenges in their models. Hence, we propose a scalable evolving inception GNN, called SEIGN. Specifically, SEIGN features two connected evolving components that adapt the graph model to the arriving snapshot and capture the changing dynamics of the node embeddings, respectively. To scale up the model training, SEIGN introduces a parameter-free message passing step for DTDGs to substantially remove the data dependencies in training. Furthermore, it significantly reduces the training memory footprint and allows us to construct a succinct graph mini-batch without performing neighborhood sampling. We further optimize the proposed evolving strategies by extracting features from neighbors at varying scales to increase the expressive power of the node representations. Our experimental evaluation, on both public benchmark and real industrial datasets, demonstrates that SEIGN achieves 2%–20% improvement in Area Under Curve (AUC) and Average Precision (AP) on the prediction task over the state-of-the-art baselines. SEIGN also supports efficient graph mini-batch training and gains 2–16 times speedup in epoch computation time over the entire DTDGs.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127432030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verifying the Correctness of Analytic Query Results (Extended Abstract)","authors":"M. Nosrati, Ying Cai","doi":"10.1109/ICDE55515.2023.00374","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00374","url":null,"abstract":"This research studies the problem of enabling users to verify that the results of analytical queries such as top k they receive from a potentially untrustworthy cloud are indeed correct. Existing work shows that it is possible for a data owner to create an authentication data structure (ADS) by which a cloud can build a verification object (VO) to prove the correctness of a query result. The current technique, however, has largely ignored the computation cost in VO construction and query result verification. In this paper, we extend and integrate Intersection tree (I-tree) and Merkle hash-tree (MH-tree) to develop a new ADS called Intersection Function Merkle Hash-tree (IFMH-tree). We propose two versions of the IFMH-tree, one-signature and multi-signature, and study their performance in supporting three representative types of analytic queries, including top-k, range, and KNN queries. Our results show that the new technique outperforms the existing solution to a large extent.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130347494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}