Efficient use of the query optimizer for automated physical design
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A critical look at the TAB benchmark for physical design tools
ACM SIGMOD Record
Constrained physical design tuning
Proceedings of the VLDB Endowment
PARINDA: an interactive physical designer for PostgreSQL
Proceedings of the 13th International Conference on Extending Database Technology
Yet another algorithms for selecting bitmap join indexes
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
CORADD: correlation aware database designer for materialized views and indexes
Proceedings of the VLDB Endowment
CoPhy: a scalable, portable, and interactive index advisor for large workloads
Proceedings of the VLDB Endowment
Optimizing index deployment order for evolving OLAP
Proceedings of the 15th International Conference on Extending Database Technology
Deterministic view selection for data-analysis queries: properties and algorithms
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
An automatic physical design tool for clustered column-stores
Proceedings of the 16th International Conference on Extending Database Technology
Processing analytical queries over encrypted data
Proceedings of the VLDB Endowment
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Existing index selection tools rely on heuristics to efficiently search within the large space of alternative solutions and to minimize the overhead of using the query optimizer for cost estimation. Index selection heuristics, despite being practical, are hard to analyze and formally compute how close they get to the optimal solution. In this paper we propose a model for index selection based on Integer Linear Programming (ILP). The ILP formulation enables a wealth of combinatorial optimization techniques for providing quality guarantees, approximate solutions and even for computing optimal solutions. We present a system architecture for ILP-based index selection, in the context of commercial database systems. Our ILP-based approach offers higher solution quality, efficiency and scalability without sacrificing any of the precision offered by existing index selection tools.