Cost-based Optimization for SPARQL Property Path Queries

很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的相互作用使生命本身成为可能。这些类型的信息是图形；将人、网页或蛋白质作为图的节点，将关系（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，关注人们和他们的朋友；在万维网上，有它的页面和页面之间的链接；科学家们收集了大量蛋白质和基因相互作用的证据。***要从这些数据存储中提取有用的信息（即回答问题），可以通过向管理要回答的数据的数据库系统提出用专门的查询语言编写的问题（称为查询）来完成。最成功的查询语言是SQL，它被广泛用于查询以表格、电子表格格式存储的关系数据和信息。在过去的四十年中，这些关系数据库管理系统和SQL背后的技术已经发展到通常可以相当有效地回答SQL查询，甚至在非常大的数据集合上也是如此。这导致了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从数百万个实验结果中筛选出最相关的结果。SPARQL是RDF数据存储（类图数据）的查询语言。RDF和SPARQL是最近创建的，旨在使Web的信息能够自动探索，这一努力被命名为语义Web。但是，在SPARQL中回答查询的效率还不如在SQL中那么高。与以前使用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（查询优化和求值的步骤）。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，让人们发现新的联系，使新的Web应用程序成为可能，并帮助科学家取得新的突破。

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.