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

{"title":"Efficiently Answering Top-k Window Aggregate Queries: Calculating Coverage Number Sequences over Hierarchical Structures","authors":"Jianqiu Xu, R. C. Wong","doi":"10.1109/ICDE55515.2023.00104","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00104","url":null,"abstract":"Given a set of spatio-temporal objects, a top-k window aggregate query reports top-k tuples that are ordered with respect to the number of objects during a given time interval and within a spatial range. For example, when analyzing traffic density in a city, one wishes to retrieve top-k time intervals in a certain area that are decreasingly ordered according to the number of vehicles passing by. As simply performing sequential scan over all objects is a costly procedure, an index structure is typically built to enhance the query performance. A crucial step during the evaluation is to determine the number of objects in an arbitrary node, called coverage number sequence. This is a challenging task since objects appear and disappear at different time points such that the number of objects in the query node changes over time. Also, as a hierarchical index structure, the value of a node at high level is achieved by performing the aggregation over its child nodes. Simply enumerating all objects rooted in the query node suffers from performance issues mainly due to (i) traversing the sub-tree to retrieve a large number of time points and (ii) repeatedly performing the aggregation at certain time points. We propose an efficient approach to solve the performance issue for both R-tree and Octree and support updating for new arrival data objects being inserted into the index. Our approach outperforms alternative methods in general according to a thorough analysis on the complexity. Coverage number sequences as well as proposed optimization techniques are utilized to enhance the performance of window aggregate queries. We confirm the superiority of our approach over alternative methods by performing a comprehensive experimental evaluation over large real datasets in a database system.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"6 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":"115132009","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":"Efficient Regular Expression Matching Based on Positional Inverted Index : (Extended Abstract)","authors":"Tao Qiu, Xiaochun Yang, Bin Wang, Wei Wang","doi":"10.1109/ICDE55515.2023.00356","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00356","url":null,"abstract":"We study the efficient regular expression (regex) matching problem. Existing algorithms are scanning-based algorithms that typically use an equivalent automaton compiled from the regex query to verify a document. Although some works propose various strategies to quickly jump to candidate locations in a document where a query result may appear, they still need to utilize the scanning-based method to verify these candidate locations. These methods become inefficient when there are still many candidate locations needed to be verified. In this paper, we propose a novel approach to efficiently compute all matching positions for a regex query purely based on a positional q-gram inverted index. We propose a gram-driven NFA to represent the language of a regex and show all regex matching locations can be obtained by finding positions on q-grams of GNFA that satisfy certain positional constraints. Then we propose several GNFA-based query plans to answer the query using the positional inverted index. In order to improve the query efficiency, we design the algorithm to build a tree-based query plan by carefully choosing a checking order for positional constraints. Experimental results on real-world datasets show that our method outperforms state-of-the-art methods by up to an order of magnitude in query efficiency.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"30 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":"115225798","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":"Graph-based Tool for Exploring PubMed Knowledge Base","authors":"Simone Bottoni, Alberto Trombetta, Flavio Bertini, D. Montesi, Francesca Bonin, A. Pascale, Martin Gleize, Pierpaolo Tommasi","doi":"10.1109/ICDE55515.2023.00280","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00280","url":null,"abstract":"Studies have shown that data retrieval and visualization tools can help health professionals to improve their understanding and communication with patients, their relationship with stakeholders, and their decision-making process. However, not many efforts have been made in this direction. In this paper, we present a prototype system for the indexing, annotation, and visualization of the PubMed knowledge base to enable the search and retrieval of health-related evidence. The proposed tool builds and keeps updated an enriched graph based on PubMed articles associating them with concepts extracted from the Unified Medical Language System (UMLS) Metathesaurus. Moreover, it allows a full-text search and graph-based navigation and supports an overview of concepts and related publications. The proposed architecture enables scale-up thanks to its containerized nature and parallelization capabilities. The code is open-source under the Apache V2 license.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"38 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":"123116679","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":"Looking Back on the Past: Active Learning with Historical Evaluation Results : Extended Abstract","authors":"Jing Yao, Zhicheng Dou, J. Nie, Ji-rong Wen","doi":"10.1109/ICDE55515.2023.00346","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00346","url":null,"abstract":"Active learning is effective for tasks with limited labeled data by annotating a small set of data actively. It utilizes the current trained model to evaluate all unlabeled samples and annotates the best samples scored by a specific query strategy to update the underlying model iteratively. Most active learning approaches rely on only the current evaluation score but ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two heuristic features of the historical evaluation results, i.e. the weighted sum and the fluctuation of history sequences. Next, to make fuller use of the information contained in the historical results, we design a query strategy that learns to select samples based on the history sequence automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. Experimental results show that our methods significantly promote existing methods.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"20 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":"116668182","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":"Bipartite Graph based Multi-view Clustering (Extended Abstract)","authors":"Lusi Li, Haibo He","doi":"10.1109/ICDE55515.2023.00371","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00371","url":null,"abstract":"In existing graph-based multi-view clustering algorithms, consensus cluster structures are explored by constructing similarity graphs of multiple views and then fusing them into a unified superior graph. However, they overlook consensus information when learning each graph independently, resulting in the undesirable unified graph with biases. To this end, we proposed a framework named bipartite graph based multi-view clustering (BIGMC) in [1] to tackle this challenge. To summarize, the key idea of BIGMC is to employ a small number of uniform anchors to represent the consensus information across views. In this way, BIGMC creates a bipartite graph between data points and anchors for each view, which are then fused to generate a unified bipartite graph. The unified graph would in turn improve each view bipartite graph and the anchor set. Finally, the clusters are formed directly using the unified graph. In this extended abstract, we also summarize the effectiveness of BIGMC as shown in experimental results originally presented in [1].","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"2016 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":"127535446","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":"Human-AI Complex Task Planning","authors":"Sepideh Nikookar","doi":"10.1109/ICDE55515.2023.00382","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00382","url":null,"abstract":"The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interests (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of my research is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints.I present a set of computational frameworks for automated task planning as a sequence generation problem that requires minimal inputs from the end users and produces personalized task plans in an uncertain environment while satisfying multiple objectives and complex constraints. At the core, I propose a set of multi-objective optimization problems with constraints, solving which will generate task plans as a sequence of sub-tasks that are highly dependent and optimize the underlying problems. From the algorithmic standpoint, I design novel algorithms by adapting Reinforcement Learning (RL) and discrete optimization-based techniques with theoretical guarantees. I also study data engineering and data management opportunities to design scalable algorithms. Finally, I provide large-scale synthetic and real-world experiments, as well as deployment challenges in the real-world environment.","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":"124961284","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":"REncoder: A Space-Time Efficient Range Filter with Local Encoder","authors":"Ziwei Wang, Zheng Zhong, Jiarui Guo, Yuhan Wu, Haoyu Li, Tong Yang, Yaofeng Tu, Huanchen Zhang, Bin Cui","doi":"10.1109/ICDE55515.2023.00158","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00158","url":null,"abstract":"A range filter is a data structure to answer range membership queries. Range queries are common in modern applications, and range filters have gained rising attention for improving the performance of range queries by ruling out empty range queries. However, state-of-the-art range filters, such as SuRF and Rosetta, suffer either high false positive rate or low throughput. In this paper, we propose a novel range filter, called REncoder. It organizes all prefixes of keys into a segment tree, and locally encodes the segment tree into a Bloom filter to accelerate queries. REncoder supports diverse workloads by adaptively choosing how many levels of the segment tree to store. We theoretically prove that the error of REncoder is bounded and derive the asymptotic space complexity under the bounded error. We conduct extensive experiments on both synthetic datasets and real datasets. The experimental results show that REncoder outperforms all state-of-the-art range filters.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"51 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":"123398245","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":"M2G4RTP: A Multi-Level and Multi-Task Graph Model for Instant-Logistics Route and Time Joint Prediction","authors":"Tianyu Cai, Huaiyu Wan, Fan Wu, Haomin Wen, S. Guo, Lixia Wu, Haoyuan Hu, Youfang Lin","doi":"10.1109/ICDE55515.2023.00253","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00253","url":null,"abstract":"Instant-logistics (e.g., food delivery and package pick-up) is increasingly calling for Route and Time Prediction (RTP), which aims to predict both future route and arrival time of a courier’s unvisited locations. Accurate RTP can greatly benefit the platform, such as optimizing order dispatching and improving user experience. Although recent years have witnessed various works for solving the RTP problem, they still suffer from the following three limitations: i) Failing to consider the high-level transfer mode of couriers between AOIs (Areas Of Interest, such as residential quarters or office buildings), which can help to build more accurate RTP. ii) Failing to simultaneously make the route and time prediction. Existing works either separately predict route/time or predict them in a two-step way. However, since route and time are strongly correlated (nearby locations in the route should have similar arrival times), jointly predicting them should be more effective. iii) The widely adopted tree-based or sequence-based architecture fails to fully encode the spatial relationship between different locations. To address the above limitations, we propose a multi-level and multi-task graph model, named M2G4RTP, for instant-logistics route and time joint prediction. Specifically, we propose a multi-level graph encoder equipped with a newly-designed GAT-e encoding module to capture couriers’ both high-level transfer modes between AOIs and low-level transfer modes between locations. Moreover, a multi-task decoder is presented to jointly predict the route and time at different levels. Finally, a loss weighting method based on homoscedastic uncertainty is designed to balance the two tasks adaptively. Extensive experiments on an industry-scale real-world dataset, as well as the online deployment on Cainiao Alibaba, demonstrate the superiority of our proposed model.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"67 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":"126608771","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":"InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs","authors":"Dalong Zhang, Xianzheng Song, Zhiyang Hu, Yang Li, Miao Tao, Binbin Hu, Lin Wang, Zhiqiang Zhang, Jun Zhou","doi":"10.1109/ICDE55515.2023.00248","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00248","url":null,"abstract":"With the rapid development of Graph Neural Networks (GNNs), more and more studies focus on system design to improve training efficiency while ignoring the efficiency of GNN inference. Actually, GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge graphs, inconsistency caused by stochastic acceleration strategies (e.g., sampling), and the serious redundant computation issue. To address the above challenges, we propose a scalable system named InferTurbo to boost the GNN inference tasks in industrial scenarios. Inspired by the philosophy of \"think-like-a-vertex\", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference. The computation of GNNs is expressed in an iteration manner, in which a vertex would gather messages via in-edges and update its state information by forwarding an associated layer of GNNs with those messages and then send the updated information to other vertexes via out-edges. Following the schema, the proposed InferTurbo can be built with alternative backends (e.g., batch processing system or graph computing system). Moreover, InferTurbo introduces several strategies like shadow-nodes and partial-gather to handle nodes with large degrees for better load balancing. With InferTurbo, GNN inference can be hierarchically conducted over the full graph without sampling and redundant computation. Experimental results demonstrate that our system is robust and efficient for inference tasks over graphs containing some hub nodes with many adjacent edges. Meanwhile, the system gains a remarkable performance compared with the traditional inference pipeline, and it can finish a GNN inference task over a graph with tens of billions of nodes and hundreds of billions of edges within 2 hours.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"148 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":"116052319","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":"Instant Representation Learning for Recommendation over Large Dynamic Graphs","authors":"Cheng Wu, Chao-Hong Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang Song, Kai Zheng, Xiaowei Wang, Guorui Zhou","doi":"10.1109/ICDE55515.2023.00014","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00014","url":null,"abstract":"Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i.e., the graph evolves rapidly over time, with various types of nodes and edges added or deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing methods neglect such streaming dynamics and thus need to be retrained once the graph has significantly evolved, making them unsuitable in the online learning environment. Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models. To this end, we propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture, SUPA develops a sample-update-propagate architecture to alleviate neighborhood disturbance. Specifically, for each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. Furthermore, to train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs. Extensive experimental results on six real-world datasets show that SUPA has a good generalization ability and is superior to sixteen state-of-the-art baseline methods. The source code is available at https://github.com/shatter15/SUPA.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"285 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":"116103792","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}