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

Visualizing Spreadsheet Formula Graphs Compactly 可视化电子表格公式图形紧凑

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611613

Fanchao Chen, Dixin Tang, Haotian Li, Aditya G. Parameswaran

{"title":"Visualizing Spreadsheet Formula Graphs Compactly","authors":"Fanchao Chen, Dixin Tang, Haotian Li, Aditya G. Parameswaran","doi":"10.14778/3611540.3611613","DOIUrl":"https://doi.org/10.14778/3611540.3611613","url":null,"abstract":"Spreadsheets are a ubiquitous data analysis tool, empowering non-programmers and programmers alike to easily express their computations by writing formulae alongside data. The dependencies created by formulae are tracked as formula graphs, which play a central role in many spreadsheet applications and are critical to the interactivity and usability of spreadsheet systems. Unfortunately, as formula graphs become large and complex, it becomes harder for end-users to make sense of formula graphs and trace the dependents or precedents of cells to check the accuracy of individual formulae and identify sources of errors. In this paper, we demonstrate a spreadsheet formula graph visualization tool, TACO-Lens, developed as a plugin for Microsoft Excel. Our plugin leverages TACO, our framework for compactly and efficiently representing formula graphs. TACO compresses formula graphs using a key spreadsheet property: tabular locality, which means that cells close to each other are likely to have similar formula structures. This compact representation enables end-users to more easily consume complex dependencies and reduces the response time for tracing dependents and precedents. TACO-Lens, our visualization plugin, depicts the compact representation of TACO and supports users in visually tracing dependents and precedents. In this demonstration, attendees can compare the visualizations of different formula graphs using TACO, Excel's built-in dependency tracing tool, and an approach that does not compress formula graphs, and quantitatively compare the different response time of different approaches.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"15 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":"135003658","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

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

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

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

Automatic SQL Error Mitigation in Oracle Oracle中的自动SQL错误缓解

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611568

Krishna Kantikiran Pasupuleti, Jiakun Li, Hong Su, Mohamed Ziauddin

{"title":"Automatic SQL Error Mitigation in Oracle","authors":"Krishna Kantikiran Pasupuleti, Jiakun Li, Hong Su, Mohamed Ziauddin","doi":"10.14778/3611540.3611568","DOIUrl":"https://doi.org/10.14778/3611540.3611568","url":null,"abstract":"Despite best coding practices, software bugs are inevitable in a large codebase. In traditional databases, when errors occur during query processing, they disrupt user workflow until workarounds are found and applied. Manual identification of workarounds often relies on a trial-and-error method. The process is not only time-consuming but also requires domain expertise that users are often lacking. In this paper, we propose a framework to automatically mitigate errors that occur during query compilation (including optimization and code generation) without any user intervention. An error is intercepted by the database internally, a workaround is identified for it, and the query is recompiled using the workaround. The entire process remains transparent to the user with the query being executed seamlessly. The proposed technique handles SQL errors during query compilation and provides three types of mitigation strategies - i) quickly failover to one of the readily-available historical plans for the statement ii) apply targeted error-correcting directives (hints) identified from the optimizer context at the time of the error iii) modify the global configuration of the optimizer using hints. This feature has been implemented and will be released in an upcoming version of Oracle Autonomous Database.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"18 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":"135003296","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

Natural Language Interfaces for Databases with Deep Learning 深度学习数据库的自然语言接口

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611575

George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika

{"title":"Natural Language Interfaces for Databases with Deep Learning","authors":"George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika","doi":"10.14778/3611540.3611575","DOIUrl":"https://doi.org/10.14778/3611540.3611575","url":null,"abstract":"In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"91 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":"135003302","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

Interactive Demonstration of EVA EVA互动演示

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611626

Gaurav Tarlok Kakkar, Aryan Rajoria, Myna Prasanna Kalluraya, Ashmita Raju, Jiashen Cao, Kexin Rong, Joy Arulraj

引用次数: 1