Cost-based Optimization for SPARQL Property Path Queries

SPARQL 属性路径查询的基于成本的优化

• 批准号: RGPIN-2015-04242
• 负责人: Godfrey, Parke
• 金额: $ 1.75万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679609
• 关键词: Cost; based; Optimization; SPARQL; Property

Much information comes looking like a network. People know people. Web pages link to other other web pages. Proteins interact with certain other proteins in ways that make life itself possible. These types of information are graphs; with people, web pages, or proteins as the nodes of the graph, and relationships (e.g., "knows"), links, or interactions as the edges of the graph. Useful data of this form is being collected nowadays in copious amounts; e.g., for social networks like Facebook, following people and their friends; in the World Wide Web, with its pages and links between pages; and by scientists, collecting vast evidence of protein and gene interactions.***To extract useful information-that is, to answer questions-against such data stores is done by asking questions, called queries, that are written in a specialized query language to the database system that is managing the data to answer. A most successful query language is SQL, which is widely used to query relational data, information that stored in tabular, spreadsheet-like format. The technology behind these relational database management systems and SQL has evolved over the last forty years to where SQL queries can often be answered quite efficiently, even over very large data collections. This has led the way to database systems being used behind the scenes in almost every aspect of our daily lives, from keeping proper track of our financial statements and transactions to enabling doctors and scientists to sift effortlessly through millions of experimental results to find the most relevant ones.***SPARQL is a query language for RDF data stores (graph-like data). RDF and SPARQL were created recently, designed to enable the Web's information to be automatically explored, within an effort that has been named the Semantic Web. But answering queries posed in SPARQL is not nearly as efficient as in SQL, yet. As was with SQL before, hard work is now needed for us to learn how to improve the process of answering SPARQL queries (the steps of query optimization and evaluation).***This work is about accomplishing that. SPARQL can then become a truly useful tool to query graph data. This would make exploring graph information much easier, letting people discover new connections, enabling new Web applications not before possible, and helping scientists to make new breakthroughs.

许多信息看起来像一个网络。人们互相了解网页链接到其他网页。蛋白质与某些其他蛋白质相互作用，使生命本身成为可能。这些类型的信息是图;其中人、网页或蛋白质作为图的节点，并且关系（例如，“知道”）、链接或交互作为图的边。这种形式的有用数据现在正在大量收集;例如，对于像Facebook这样的社交网络来说，它可以跟踪人们和他们的朋友;在万维网上，它的页面和页面之间的链接;以及科学家，收集蛋白质和基因相互作用的大量证据。要提取有用的信息，也就是回答问题，针对这样的数据存储是通过提出问题来完成的，这些问题称为查询，这些问题以专门的查询语言编写到管理要回答的数据的数据库系统。最成功的查询语言是SQL，它被广泛用于查询关系数据，即以表格、电子表格等格式存储的信息。这些关系数据库管理系统和SQL背后的技术在过去的四十年里已经发展到SQL查询通常可以非常有效地回答，即使是在非常大的数据集合上。这导致数据库系统在我们日常生活的几乎每个方面都在幕后使用，从正确跟踪我们的财务报表和交易，到使医生和科学家能够毫不费力地筛选数百万个实验结果，以找到最相关的结果。SPARQL是一种用于RDF数据存储（类似于图的数据）的查询语言。RDF和SPARQL是最近创建的，旨在使Web的信息能够在被称为语义Web的努力中被自动探索。但是在SPARQL中回答查询的效率还不如SQL。与以前的SQL一样，我们现在需要努力学习如何改进回答SPARQL查询的过程（查询优化和评估的步骤）。这项工作就是为了实现这一点。SPARQL可以成为查询图数据的真正有用的工具。这将使探索图形信息变得更加容易，让人们发现新的连接，启用新的Web应用程序，并帮助科学家取得新的突破。