Proceedings of the Vldb Endowment最新文献_第5页

QO-Insight: Inspecting Steered Query Optimizers QO-Insight:检查导向查询优化器

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611586

Christoph Anneser, Mario Petruccelli, Nesime Tatbul, David Cohen, Zhenggang Xu, Prithviraj Pandian, Nikolay Laptev, Ryan Marcus, Alfons Kemper

引用次数: 1

KGNav: A Knowledge Graph Navigational Visual Query System KGNav:知识图谱导航可视化查询系统

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611592

Xiang Wang, Xin Wang, Zhaozhuo Li, Dong Han

{"title":"KGNav: A Knowledge Graph Navigational Visual Query System","authors":"Xiang Wang, Xin Wang, Zhaozhuo Li, Dong Han","doi":"10.14778/3611540.3611592","DOIUrl":"https://doi.org/10.14778/3611540.3611592","url":null,"abstract":"Visual query is a vital technique for comprehending and analyzing knowledge graphs, which provides an effective method to lower the barrier of querying knowledge graphs for non-professional users. Nevertheless, visual query techniques for knowledge graphs and ontologies that have emerged in recent years cannot bridge the gap between global information provided by the knowledge graph schema and underlying data of knowledge graph. Thus it cannot fully exploit the global information to navigate users for querying knowledge graphs. This demonstration showcases KGNav, a Knowledge Graph Navigational visual query system. KGNav (1) redefines the minimal unit of operation to abstract the conceptual hierarchy, i.e., Knowledge Graph Schema, in the domain from the original knowledge graph in an offline semi-automatic way through the equivalence relations between these units; it also (2) provides a series of operators and an interactive GUI to capture user query intentions, guiding users to explore the Knowledge Graph Schema to achieve in-depth analysis of knowledge graphs. We will demonstrate the capability of KGNav in reducing tedious queries, enabling users to swiftly grasp the structure of the knowledge graph, and performing queries through several fundamental scenarios.","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":"134997921","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

Ganos Aero: A Cloud-Native System for Big Raster Data Management and Processing Ganos Aero:用于大栅格数据管理和处理的云原生系统

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611597

Fei Xiao, Jiong Xie, Zhida Chen, Feifei Li, Zhen Chen, Jianwei Liu, Yinpei Liu

{"title":"Ganos Aero: A Cloud-Native System for Big Raster Data Management and Processing","authors":"Fei Xiao, Jiong Xie, Zhida Chen, Feifei Li, Zhen Chen, Jianwei Liu, Yinpei Liu","doi":"10.14778/3611540.3611597","DOIUrl":"https://doi.org/10.14778/3611540.3611597","url":null,"abstract":"The development of Earth Observation technology contributes to the production of massive raster data. It is vital to manage and conduct analytical tasks on the raster data. Existing solutions employ dedicated systems for the raster data management and processing, respectively, incurring problems such as data redundancy, difficulty in updating, expensive data transferring and transformation, etc. To cope with these limitations, this demonstration presents Ganos Aero, a cloud-native system for big raster data management and processing. Ganos Aero proposes a unified raster data model for both the data management and processing, which stores a single copy of the raster data and without performing an expensive tiling procedure, and thus achieves significant improvement in the storage and updating efficiency. To enable efficient query and batch task processing, Ganos Aero implements an on-the-fly tile production mechanism, and optimizes its performance using the cloud features including decoupling compute from storage and pushing costly operations closer to the storage layer. Since deployed in Alibaba Cloud in 2022, Ganos Aero has been playing a critical role in many real applications including the modern agriculture, environment monitoring and protection, et al.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"47 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":"134997931","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

Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning openbml的演示，一个民主化的数据库内机器学习框架

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611598

Mahdi Ghorbani, Amir Shaikhha

引用次数: 0

SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting SimpleTS:一种有效且通用的时间序列预测模型选择框架

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611561

Yuanyuan Yao, Dimeng Li, Hailiang Jie, Hailiang Jie, Tianyi Li, Jie Chen, Jiaqi Wang, Feifei Li, Yunjun Gao

{"title":"SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting","authors":"Yuanyuan Yao, Dimeng Li, Hailiang Jie, Hailiang Jie, Tianyi Li, Jie Chen, Jiaqi Wang, Feifei Li, Yunjun Gao","doi":"10.14778/3611540.3611561","DOIUrl":"https://doi.org/10.14778/3611540.3611561","url":null,"abstract":"Time series forecasting, that predicts events through a sequence of time, has received increasing attention in past decades. The diverse range of time series forecasting models presents a challenge for selecting the most suitable model for a given dataset. As such, the Alibaba Cloud database monitoring system must address the issue of selecting an optimal forecasting model for a single time series data. While several model selection frameworks, including AutoAI-TS, have been developed to predict a dataset, their effectiveness may be limited as they may not adapt well to all types of time series, resulting in reduced prediction accuracy. Alternatively, models such as AutoForecast, which train on individual data points, may offer better adaptability but are limited by longer training time required. In this paper, we introduce SimpleTS, a versatile framework for time series forecasting that exhibits high efficiency and accuracy across all types of time series data. When performing an online prediction task, SimpleTS first classifies input time series into one type, and then efficiently selects the most suitable prediction model for this type. To optimize performance, SimpleTS (i) clusters models with similar performance to improve the efficiency of classification; (ii) uses soft labeling and weighted representation learning to achieve higher classification accuracy for different time series types. Extensive experiments on 3 private datasets and 52 public datasets show that SimpleTS outperforms the state-of-the-art toolkits in terms of both training time and prediction accuracy.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"14 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":"134998132","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

Solving Hard Variants of Database Schema Matching on Quantum Computers 解决量子计算机上数据库模式匹配的硬变体问题

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611603

Kristin Fritsch, Stefanie Scherzinger

引用次数: 0

ADOps: An Anomaly Detection Pipeline in Structured Logs 采用:结构化日志异常检测管道

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611618

Xintong Song, Yusen Zhu, Jianfei Wu, Bai Liu, Hongkang Wei

引用次数: 0

Portals: A Showcase of Multi-Dataflow Stateful Serverless 门户:展示多数据流的无状态服务器

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611619

Jonas Spenger, Chengyang Huang, Philipp Haller, Paris Carbone

引用次数: 1

Kora: A Cloud-Native Event Streaming Platform for Kafka Kora: Kafka的云原生事件流平台

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611567

Anna Povzner, Prince Mahajan, Jason Gustafson, Jun Rao, Ismael Juma, Feng Min, Shriram Sridharan, Nikhil Bhatia, Gopi Attaluri, Adithya Chandra, Stanislav Kozlovski, Rajini Sivaram, Lucas Bradstreet, Bob Barrett, Dhruvil Shah, David Jacot, David Arthur, Ron Dagostino, Colin McCabe, Manikumar Reddy Obili, Kowshik Prakasam, Jose Garcia Sancio, Vikas Singh, Alok Nikhil, Kamal Gupta

{"title":"Kora: A Cloud-Native Event Streaming Platform for Kafka","authors":"Anna Povzner, Prince Mahajan, Jason Gustafson, Jun Rao, Ismael Juma, Feng Min, Shriram Sridharan, Nikhil Bhatia, Gopi Attaluri, Adithya Chandra, Stanislav Kozlovski, Rajini Sivaram, Lucas Bradstreet, Bob Barrett, Dhruvil Shah, David Jacot, David Arthur, Ron Dagostino, Colin McCabe, Manikumar Reddy Obili, Kowshik Prakasam, Jose Garcia Sancio, Vikas Singh, Alok Nikhil, Kamal Gupta","doi":"10.14778/3611540.3611567","DOIUrl":"https://doi.org/10.14778/3611540.3611567","url":null,"abstract":"Event streaming is an increasingly critical infrastructure service used in many industries and there is growing demand for cloud-native solutions. Confluent Cloud provides a massive scale event streaming platform built on top of Apache Kafka with tens of thousands of clusters running in 70+ regions across AWS, Google Cloud, and Azure. This paper introduces Kora , the cloud-native platform for Apache Kafka at the core of Confluent Cloud. We describe Kora's design that enables it to meet its cloud-native goals, such as reliability, elasticity, and cost efficiency. We discuss Kora's abstractions which allow users to think in terms of their workload requirements and not the underlying infrastructure, and we discuss how Kora is designed to provide consistent, predictable performance across cloud environments with diverse capabilities.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"46 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":"135002986","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

Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 加入订单选择与深度强化学习:基础，技术和挑战

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611576

Zhengtong Yan, Valter Uotila, Jiaheng Lu

{"title":"Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges","authors":"Zhengtong Yan, Valter Uotila, Jiaheng Lu","doi":"10.14778/3611540.3611576","DOIUrl":"https://doi.org/10.14778/3611540.3611576","url":null,"abstract":"Join Order Selection (JOS) is a fundamental challenge in query optimization, as it significantly affects query performance. However, finding an optimal join order is an NP-hard problem due to the exponentially large search space. Despite the decades-long effort, traditional methods still suffer from limitations. Deep Reinforcement Learning (DRL) approaches have recently gained growing interest and shown superior performance over traditional methods. These DRL-based methods could leverage prior experience through the trial-and-error strategy to automatically explore the optimal join order. This tutorial will focus on recent DRL-based approaches for join order selection by providing a comprehensive overview of the various approaches. We will start by briefly introducing the core concepts of join ordering and the traditional methods for JOS. Next, we will provide some preliminary knowledge about DRL and then delve into DRL-based join order selection approaches by offering detailed information on those methods, analyzing their relationships, and summarizing their weaknesses and strengths. To help the audience gain a deeper understanding of DRL approaches for JOS, we will present two open-source demonstrations and compare their differences. Finally, we will identify research challenges and open problems to provide insights into future research directions. This tutorial will provide valuable guidance for developing more practical DRL approaches for JOS.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"9 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":"135002991","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