CAREER: Architecting A Database Management System for Semantic Web Data

职业：为语义 Web 数据构建数据库管理系统

• 批准号: 0845643
• 负责人: Daniel Abadi
• 金额: $ 40万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845643&HistoricalAwards=false
• 关键词: CAREER; Architecting; Database; Management; System

The goal of the Semantic Web is to free Web data from the applications that control them, so that data can be easily described and exchanged. This is accomplished by supplementing natural language and other data found on the Web with machine readable metadata in statement form (e.g., X is-a person, X has-name Joe, X has-age 35) and enabling descriptions of data ontologies so that data from different applications can be integrated through ontology mapping. One element of this vision is to turn the Web into a giant database, against which one can issue structured queries and receive structured answers in response.The SW-Store project is undertaking the clean-slate design of a DBMS specifically architected for this type of Web metadata and the prevalent Semantic Web data model, the Resource Description Framework, or RDF. The management of Semantic Web data presents many difficult challenges. The size of the data is growing rapidly, and in theory could reach the scale of the Web. The types of queries vary greatly in complexity, ranging from keyword search to complicated parameterized subgraph matching. Data integration, inference, and reasoning must be primitive operations that can operate at scale without human intervention. A data management system must not only be a place where data is stored and from which data is accessed; it must use the machine-readable semantics of the data to develop higher level models and help guide a user through the mass of information. In sum, a data management system for the Semantic Web will look very different from a standard, transactional, relational database system.The SW-store project researches the architecture of such a system. This research is inherently interdisciplinary, bringing in ideas from the data management, Semantic Web, and artificial intelligence communities. The project involves experimenting with partitioning schemes, where data is allocated to different nodes on a shared-nothing cluster so that queries can be run in parallel across multiple machines. It also involves exploring how ontology reasoning can be integrated inside the database system so that it can benefit from the near limitless scalability a shared-nothing cluster can offer. SW-Store further investigates providing iterative query interfaces and integrating complex queries with text search. Finally, the project involves studying the design of the storage layer for a Semantic Web data management system, looking at how data should be laid out, updates should be performed, and what materialized views to create.Further information about the project can be found at the project Webpage: http://db.cs.yale.edu/swstore/

语义Web的目标是将Web数据从控制它们的应用程序中解放出来，以便可以轻松地描述和交换数据。这是通过用语句形式的机器可读元数据（例如，X是一个人，X的名字是Joe，X的年龄是35岁），并支持对数据本体的描述，从而可以通过本体映射来集成来自不同应用程序的数据。这一愿景的一个要素是将Web变成一个巨大的数据库，人们可以对它发出结构化的查询并接收结构化的响应。SW-Store项目正在进行一个DBMS的全新设计，该DBMS专门为这种类型的Web元数据和流行的语义Web数据模型（资源描述框架或RDF）而构建。语义Web数据的管理提出了许多困难的挑战。数据的规模正在迅速增长，理论上可以达到Web的规模。查询的类型在复杂性上变化很大，从关键字搜索到复杂的参数化子图匹配。数据集成、推断和推理必须是原始操作，可以在没有人为干预的情况下大规模操作。数据管理系统不仅是存储数据和访问数据的地方;它还必须使用数据的机器可读语义来开发更高级别的模型，并帮助引导用户浏览大量信息。总之，语义Web的数据管理系统看起来与标准的、事务性的、关系数据库系统有很大的不同，SW-store项目研究了这样一个系统的体系结构。这项研究本质上是跨学科的，从数据管理，语义网和人工智能社区带来的想法。该项目涉及对分区方案的试验，其中数据被分配到无共享集群上的不同节点，以便查询可以在多台机器上并行运行。它还涉及探索本体推理如何集成到数据库系统中，以便它可以从无共享集群提供的近乎无限的可扩展性中受益。SW-Store进一步研究提供迭代查询接口，并将复杂查询与文本搜索集成。最后，该项目还包括研究语义Web数据管理系统的存储层设计，研究数据应如何布局、应如何执行更新以及应创建哪些物化视图。有关该项目的更多信息，请访问该项目的网页：http://db.cs.yale.edu/swstore/