Composing, optimizing, and executing plans for bioinformatics web services

  • Authors:

  • Snehal Thakkar;Luis Ambite;A. Knoblock

  • Affiliations:

  • Information Sciences Institute, University of Southern California, USA;Information Sciences Institute, University of Southern California, USA;Information Sciences Institute, University of Southern California, USA

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2005

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Abstract

The emergence of a large number of bioinformatics datasets on the Internet has resulted in the need for flexible and efficient approaches to integrate information from multiple bioinformatics data sources and services. In this paper, we present our approach to automatically generate composition plans for web services, optimize the composition plans, and execute these plans efficiently. While data integration techniques have been applied to the bioinformatics domain, the focus has been on answering specific user queries. In contrast, we focus on automatically generating parameterized integration plans that can be hosted as web services that respond to a range of inputs. In addition, we present two novel techniques that improve the execution time of the generated plans by reducing the number of requests to the existing data sources and by executing the generated plan more efficiently. The first optimization technique, called tuple-level filtering, analyzes the source/service descriptions in order to automatically insert filtering conditions in the composition plans that result in fewer requests to the component web services. To ensure that the filtering conditions can be evaluated, this technique may include sensing operations in the integration plan. The savings due to filtering significantly exceed the cost of the sensing operations. The second optimization technique consists in mapping the integration plans into programs that can be executed by a dataflow-style, streaming execution engine. We use real-world bioinformatics web services to show experimentally that (1) our automatic composition techniques can efficiently generate parameterized plans that integrate data from large numbers of existing services and (2) our optimization techniques can significantly reduce the response time of the generated integration plans.