An Improved Depth-First Control Strategy for Query-Subquery Nets in Evaluating Queries to Horn Knowledge Bases

Conference paper

DOI: 10.1007/978-3-319-06569-4_21

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 282)
Cite this paper as:
Cao S.T., Nguyen L.A. (2014) An Improved Depth-First Control Strategy for Query-Subquery Nets in Evaluating Queries to Horn Knowledge Bases. In: van Do T., Thi H., Nguyen N. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 282. Springer, Cham

Abstract

The QSQN evaluation method uses query-subquery nets and allows any control strategy for processing queries to Horn knowledge bases. This paper proposes an improved depth-first control strategy for the QSQN evaluation method to reduce the number of accesses to the intermediate relations and extensional relations. We came up to the improvement by using query-subquery nets to observe which relations are likely to grow or saturate and which ones are not yet affected by the computation and the other relations. Our intention is to accumulate as many as possible tuples or subqueries at each node of the query-subquery net before processing it. The experimental results confirm the outperformance of the improved version.

Keywords

Horn knowledge bases deductive databases query processing Magic-Set transformation QSQ QSQR QSQN 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Information TechnologyVinh UniversityVinhVietnam
  2. 2.Institute of InformaticsUniversity of WarsawWarsawPoland
  3. 3.Faculty of Information TechnologyVNU University of Engineering and TechnologyHanoiVietnam

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