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The like regular expression predicate has been part of the SQL standard sinceat least 1989. However, despite its popularity and wide usage, database vendorsprovide only limited indexing support for regular expression queries whichalmost always require a full table scan.  In this paper we propose a rigorous and robust approach for providingindexing support for regular expression queries. Our approach consists offormulating the indexing problem as a combinatorial optimization problem. Webegin with a database, abstracted as a collection of strings. From this dataset we generate a query workload. The input to the optimization problem is thedatabase and the workload. The output is a set of multigrams (substrings) whichcan be used as keys to records which satisfy the query workload. The multigramscan then be integrated with the data structure (like B+ trees) to provideindexing support for the queries. We provide a deterministic and a randomizedapproximation algorithm (with provable guarantees) to solve the optimizationproblem. Extensive experiments on synthetic data sets demonstrate that ourapproach is accurate and efficient.  We also present a case study on PROSITE patterns - which are complex regularexpression signatures for classes of proteins. Again, we are able todemonstrate the utility of our indexing approach in terms of accuracy andefficiency. Thus, perhaps for the first time, there is a robust and practicalindexing mechanism for an important class of database queries.

An index for regular expression queries: Design and implementation