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Maximizing conjunctive views in deletion propagation

Published:13 June 2011Publication History

ABSTRACT

In deletion propagation, tuples from the database are deleted in order to reflect the deletion of a tuple from the view. Such an operation may result in the (often necessary) deletion of additional tuples from the view, besides the intentionally deleted one. The complexity of deletion propagation is studied, where the view is defined by a conjunctive query (CQ), and the goal is to maximize the number of tuples that remain in the view. Buneman et al. showed that for some simple CQs, this problem can be solved by a trivial algorithm. This paper identifies additional cases of CQs where the trivial algorithm succeeds, and in contrast, it proves that for some other CQs the problem is NP-hard to approximate better than some constant ratio. In fact, this paper shows that among the CQs without self joins, the hard CQs are exactly the ones that the trivial algorithm fails on. In other words, for every CQ without self joins, deletion propagation is either APX-hard or solvable by the trivial algorithm.

The paper then presents approximation algorithms for certain CQs where deletion propagation is APX-hard. Specifically, two constant-ratio (and polynomial-time) approximation algorithms are given for the class of star CQs without self joins. The first algorithm is a greedy algorithm, and the second is based on randomized rounding of a linear program. While the first algorithm is more efficient, the second one has a better approximation ratio. Furthermore, the second algorithm can be extended to a significant generalization of star CQs. Finally, the paper shows that self joins can have a major negative effect on the approximability of the problem.

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      • Published in

        cover image ACM Conferences
        PODS '11: Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
        June 2011
        332 pages
        ISBN:9781450306607
        DOI:10.1145/1989284

        Copyright © 2011 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 June 2011

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