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Flexible and scalable cost-based query planning in mediators: A transformational approach

机译:中介者中基于成本的灵活,可扩展的查询计划:一种转型方法

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摘要

The Internet provides access to a wealth of information. For any given topic or application domain there are a variety of available information sources. However, current systems, such as search engines or topic directories in the World Wide Web, offer only very limited capabilities for locating, combining, and organizing information. Mediators, systems that provide integrated access and database-like query capabilities to information distributed over heterogeneous sources, are critical to realize the full potential of meaningful access to networked information. Query planning, the task of generating a cost-efficient plan that computes a user query from the relevant information sources, is central to mediator systems. However, query planning is a computationally hard problem due to the large number of possible sources and possible orderings on the operations to process the data. Moreover, the choice of sources, data processing operations, and their ordering, strongly affects the plan cost. In this paper, we present an approach to query planning in mediators based on a general planning paradigm called Planning by Rewriting (PbR) (Ambite and Knoblock, 1997). Our work yields several contributions. First, our PbR-based query planner combines both the selection of the sources and the ordering of the operations into a single search space in which to optimize the plan quality. Second, by using local search techniques our planner explores the combined search space efficiently and produces high-quality plans. Third, because our query planner is an instantiation of a domain- independent framework it is very flexible and can be extended in a principled way. Fourth, our planner has an anytime behavior. Finally, we provide empirical results showing that our PbR-based query planner compares favorably on scalability and plan quality over previous approaches, which include both classical AI planning and dynamic-programming query optimization techniques.
机译:互联网提供对大量信息的访问。对于任何给定的主题或应用程序领域,都有各种可用的信息源。但是,当前的系统(例如,万维网中的搜索引擎或主题目录)仅提供非常有限的功能来定位,组合和组织信息。中介器是对分布在异构源上的信息提供集成访问和类似数据库的查询功能的系统,对于实现对网络信息的有意义访问的全部潜力至关重要。查询计划是生成成本有效计划的任务,该计划可从相关信息源中计算用户查询,这对调解员系统至关重要。但是,由于大量可能的来源和对数据进行处理的操作的可能顺序,查询计划是一个计算难题。此外,源的选择,数据处理操作及其顺序会严重影响计划成本。在本文中,我们提出了一种基于称为“通过重写计划”(PbR)的一般计划范式的中介程序中查询计划的方法(Ambite和Knoblock,1997)。我们的工作做出了一些贡献。首先,我们基于PbR的查询计划器将源的选择和操作的顺序都组合到单个搜索空间中,以优化计划质量。其次,通过使用本地搜索技术,我们的计划人员可以有效地探索组合的搜索空间并生成高质量的计划。第三,因为我们的查询计划器是域独立框架的实例,所以它非常灵活,可以有原则地扩展。第四,我们的计划者有随时行为。最后,我们提供的经验结果表明,基于PbR的查询计划程序在可伸缩性和计划质量方面优于传统方法,包括传统的AI计划和动态编程查询优化技术。

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