NDLI: Exact and approximate algorithms for the index selection problem in physical database design

Content Provider	IEEE Xplore Digital Library
Author	Caprara, A. Fischetti, M. Maio, D.
Copyright Year	1989
Abstract	The index selection problem (ISP) is an important optimization problem in the physical design of databases. The aim of this paper is to show that ISP, although NP-hard, can in practice be solved effectively through well-designed algorithms. We formulate ISP as a 0-1 integer linear program and describe an exact branch-and-bound algorithm based on the linear programming relaxation of the model. The performance of the algorithm is enhanced by means of procedures to reduce the size of the candidate index set. We also describe heuristic algorithms based on the solution of a suitably defined knapsack subproblem and on Lagrangian decomposition. Finally, computational results on several classes of test problems are given. We report the exact solution of large-scale ISP instances involving several hundred indexes and queries. We also evaluate one of the heuristic algorithms we propose on very large-scale instances involving several thousand indexes and queries and show that it consistently produces very tight approximate (and sometimes provably optimal) solutions. Finally, we discuss possible extensions and future directions of research.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	955
Ending Page	967
Page Count	13
File Size	1596536
File Format	PDF
ISSN	10414347
Volume Number	7
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexes Algorithm design and analysis Heuristic algorithms Relational databases Costs Large-scale systems Design optimization Lagrangian functions Testing Integer linear programming
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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