Abstract
Selecting and precomputing indexes and materialized views, with the goal of improving query-processing performance, is an important part of database-performance tuning. The significant complexity of the view- and index-selection problem may result in high total cost of ownership for database systems. In this paper, we develop efficient methods that deliver user-specified quality of the set of selected views and indexes when given view- and index-based plans as problem inputs. Here, quality means proximity to the globally optimum performance for the input query workload given the input query plans. Our experimental results and comparisons on synthetic and benchmark instances demonstrate the competitiveness of our approach and show that it provides a winning combination with end-to-end view- and index-selection frameworks such as those of [1, 2].
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