Abstract
This chapter presents self-adaptive set of histograms (SASH) that addresses the problem of building and maintaining a set of histograms. SASH is a novel two-phase method that builds and maintains an optimal set of histograms using only query feedback information from a multidimensional query workload, without scanning the database. SASH has also provided a unified framework that addresses the problem of which attribute sets to build histograms on, the problem of allocating memory to a set of histograms, and the problem of tuning a set of histograms to the query workload. In the online tuning phase, the current set of histograms is tuned in response to the estimation error of each query in an online manner. In the restructuring phase, a new and more accurate set of histograms replaces the current set of histograms. The new set of histograms is found using information from a batch of query feedback. The chapter presents experimental results that show the effectiveness and accuracy of approach.
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