Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.
This article presents a method for adaptively representing multidimensional data cubes using wavelet view elements in order to more efficiently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of wavelet view elements. The view elements differ from traditional data cube cells in that they correspond to partial and residual aggregations of the data cube. The view elements provide highly granular building blocks for synthesizing the aggregated and range-aggregated views of the data cubes. We propose a strategy for selectively materializing alternative sets of view elements based on the patterns of access of views. We present a fast and optimal algorithm for selecting a non-expansive set of wavelet view elements that minimizes the average processing cost for supporting a population of queries of data cube views. We also present a greedy algorithm for allowing the selective materialization of a redundant set of view element sets which, for measured increases in storage capacity, further reduces processing costs. Experiments and analytic results show that the wavelet view element framework performs better in terms of lower processing and storage cost than previous methods that materialize and store redundant views for online analytical processing (OLAP).