text: The like regular expression predicate has been part of the SQL standard since at least 1989. However, despite its popularity and wide usage, database vendors provide only limited indexing support for regular expression queries which almost always require a full table scan. In this paper we propose a rigorous and robust approach for providing indexing support for regular expression queries. Our approach consists of formulating the indexing problem as a combinatorial optimization problem. We begin with a database, abstracted as a collection of strings. From this data set we generate a query workload. The input to the optimization problem is the database and the workload. The output is a set of multigrams (substrings) which can be used as keys to records which satisfy the query workload. The multigrams can then be integrated with the data structure (like B+ trees) to provide indexing support for the queries. We provide a deterministic and a randomized approximation algorithm (with provable guarantees) to solve the optimization problem. Extensive experiments on synthetic data sets demonstrate that our approach is accurate and efficient. We also present a case study on PROSITE patterns - which are complex regular expression signatures for classes of proteins. Again, we are able to demonstrate the utility of our indexing approach in terms of accuracy and efficiency. Thus, perhaps for the first time, there is a robust and practical indexing mechanism for an important class of database queries.
  @cite_5: Abstract The budgeted maximum coverage problem is: given a collection S of sets with associated costs defined over a domain of weighted elements, and a budget L , find a subset of S ′⫅ S such that the total cost of sets in S ′ does not exceed L , and the total weight of elements covered by S ′ is maximized. This problem is NP-hard. For the special case of this problem, where each set has unit cost, a (1−1 e ) -approximation is known. Yet, prior to this work, no approximation results were known for the general cost version. The contribution of this paper is a (1−1 e ) -approximation algorithm for the budgeted maximum coverage problem. We also argue that this approximation factor is the best possible, unless NP ⫅ DTIME (n O ( log log n) ) .
summary: Hore formalizes the multigram selection problem as an instance of the Budgeted Maximum Coverage (BMC) @cite_5 problem. Specifically the cover set forms the Budgeted' part while the utility of the index is captured in the Coverage' component. The main principle of the BEST algorithm is to select multigrams which increase the index hit rate so more queries can utilize the algorithm. However, a major weakness of the BEST algorithm is that it neither scales to large datasets nor large query workloads because it uses a cartesian product of query workload and database as the search space.