Proceedings of the Vldb Endowment最新文献_第8页

Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact 知识图谱的世代:疯狂的想法和商业影响

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611636

Xin Luna Dong

引用次数: 1

Efficient Execution of User-Defined Functions in SQL Queries SQL查询中用户定义函数的高效执行

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611574

Yannis Foufoulas, Alkis Simitsis

引用次数: 0

Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources Lynx:面向多个异构数据源的图形查询框架

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611587

Zhihong Shen, Chuan Hu, Zihao Zhao

引用次数: 0

PAINE Demo: Optimizing Video Selection Queries with Commonsense Knowledge PAINE演示:用常识优化视频选择查询

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611581

Wenjia He, Ibrahim Sabek, Yuze Lou, Michael Cafarella

{"title":"PAINE Demo: Optimizing Video Selection Queries with Commonsense Knowledge","authors":"Wenjia He, Ibrahim Sabek, Yuze Lou, Michael Cafarella","doi":"10.14778/3611540.3611581","DOIUrl":"https://doi.org/10.14778/3611540.3611581","url":null,"abstract":"Because video is becoming more popular and constitutes a major part of data collection, we have the need to process video selection queries --- selecting videos that contain target objects. However, a naïve scan of a video corpus without optimization would be extremely inefficient due to applying complex detectors to irrelevant videos. This demo presents Paine; a video query system that employs a novel index mechanism to optimize video selection queries via commonsense knowledge. Paine samples video frames to build an inexpensive lossy index, then leverages probabilistic models based on existing commonsense knowledge sources to capture the semantic-level correlation among video frames, thereby allowing Paine to predict the content of unindexed video. These models can predict which videos are likely to satisfy selection predicates so as to avoid Paine from processing irrelevant videos. We will demonstrate a system prototype of Paine for accelerating the processing of video selection queries, allowing VLDB'23 participants to use the Paine interface to run queries. Users can compare Paine with the baseline, the SCAN method.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134998136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChainDash: An Ad-Hoc Blockchain Data Analytics System ChainDash: Ad-Hoc区块链数据分析系统

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611611

Yushi Liu, Liwei Yuan, Zhihao Chen, Yekai Yu, Zhao Zhang, Cheqing Jin, Ying Yan

引用次数: 0

Machine Learning for Subgraph Extraction: Methods, Applications and Challenges 子图提取的机器学习:方法、应用和挑战

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611571

Kai Siong Yow, Ningyi Liao, Siqiang Luo, Reynold Cheng

{"title":"Machine Learning for Subgraph Extraction: Methods, Applications and Challenges","authors":"Kai Siong Yow, Ningyi Liao, Siqiang Luo, Reynold Cheng","doi":"10.14778/3611540.3611571","DOIUrl":"https://doi.org/10.14778/3611540.3611571","url":null,"abstract":"Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the associated original graphs, and many graph properties are known to be inherited by subgraphs. Subgraphs can be applied in many areas such as social networks, recommender systems, biochemistry and fraud discovery. Researchers from various communities have paid a great deal of attention to investigate numerous subgraph problems, by proposing algorithms that mainly extract important structures of a given graph. There are however some limitations that should be addressed, with regard to the efficiency, effectiveness and scalability of these traditional algorithms. As a consequence, machine learning techniques---one of the most latest trends---have recently been employed in the database community to address various subgraph problems considering that they have been shown to be beneficial in dealing with graph-related problems. We discuss learning-based approaches for four well known subgraph problems in this tutorial, namely subgraph isomorphism, maximum common subgraph, community detection and community search problems. We give a general description of each proposed model, and analyse its design and performance. To allow further investigations on relevant subgraph problems, we suggest some potential future directions in this area. We believe that this work can be used as one of the primary resources, for researchers who intend to develop learning models in solving problems that are closely related to subgraphs.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134998301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MINT: Detecting Fraudulent Behaviors from Time-Series Relational Data MINT:从时间序列关系数据中检测欺诈行为

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611551

Fei Xiao, Yuncheng Wu, Meihui Zhang, Gang Chen, Beng Chin Ooi

{"title":"MINT: Detecting Fraudulent Behaviors from Time-Series Relational Data","authors":"Fei Xiao, Yuncheng Wu, Meihui Zhang, Gang Chen, Beng Chin Ooi","doi":"10.14778/3611540.3611551","DOIUrl":"https://doi.org/10.14778/3611540.3611551","url":null,"abstract":"The e-commerce platforms, such as Shopee, have accumulated a huge volume of time-series relational data, which contains useful information on differentiating fraud users from benign users. Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as a single intention without considering the temporal behavioral patterns, row-level interactions, and different view intentions. In this paper, we present MINT, a M ultiview row- IN teractive T ime-aware framework to detect fraudulent behaviors from time-series structured data. The key idea of MINT is to build a time-aware behavior graph for each user's time-series relational data with each row represented as an action node. We utilize the user's temporal information to construct three different graph convolutional matrices for hierarchically learning the user's intentions from different views, that is, short-term, medium-term, and long-term intentions. To capture more meaningful row-level interactions and alleviate the over-smoothing issue in a vanilla time-aware behavior graph, we propose a novel gated neighbor interaction mechanism to calibrate the aggregated information by each action node. Since the receptive fields of the three graph convolutional layers are designed to grow nearly exponentially, our MINT requires many fewer layers than traditional deep graph neural networks (GNNs) to capture multi-hop neighboring information, and avoids recurrent feedforward propagation, thus leading to higher training efficiency and scalability. Our extensive experiments on the large-scale e-commerce datasets from Shopee with up to 4.6 billion records and a public dataset from Amazon show that MINT achieves superior performance over 10 state-of-the-art models and provides better interpretability and scalability.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134998306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Clustering of Multivariate Time Series with Time2Feat 基于Time2Feat的多元时间序列可解释聚类

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611604

Angela Bonifati, Francesco Del Buono, Francesco Guerra, Miki Lombardi, Donato Tiano

引用次数: 0

DuckPGQ: Bringing SQL/PGQ to DuckDB DuckPGQ:将SQL/PGQ引入DuckDB

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611614

Daniel ten Wolde, Gábor Szárnyas, Peter Boncz

引用次数: 0

AutoSteer: Learned Query Optimization for Any SQL Database 自动驾驶:学习查询优化任何SQL数据库

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611544

Christoph Anneser, Nesime Tatbul, David Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus

{"title":"AutoSteer: Learned Query Optimization for Any SQL Database","authors":"Christoph Anneser, Nesime Tatbul, David Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus","doi":"10.14778/3611540.3611544","DOIUrl":"https://doi.org/10.14778/3611540.3611544","url":null,"abstract":"This paper presents AutoSteer, a learning-based solution that automatically drives query optimization in any SQL database that exposes tunable optimizer knobs. AutoSteer builds on the Bandit optimizer (Bao) and extends it with new capabilities (e.g., automated hint-set discovery) to minimize integration effort and facilitate usability in both monolithic and disaggregated SQL systems. We successfully applied AutoSteer on PostgreSQL, PrestoDB, Spark-SQL, MySQL, and DuckDB - five popular open-source database engines with diverse query optimizers. We then conducted a detailed experimental evaluation with public benchmarks (JOB, Stackoverflow, TPC-DS) and a production workload from Meta's PrestoDB deployments. Our evaluation shows that AutoSteer can not only outperform these engines' native query optimizers (e.g., up to 40% improvements for PrestoDB) but can also match the performance of Bao-for-PostgreSQL with reduced human supervision and increased adaptivity, as it replaces Bao's static, expert-picked hint-sets with those that are automatically discovered. We also provide an open-source implementation of AutoSteer together with a visual tool for interactive use by query optimization experts.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1