Dynamic assembly of views in data cubes

Abstract

In this paper, we present a method for dynamically assembling views in multi-dimensional data cubes in order to more efficiently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of 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 selecting and materializing the view elements based on the frequency of view access. This allows the dynamic adaptation of the view element sets to patterns of retrieval. We present a fast and optimal algorithm for selecting non-expansive view element sets that minimize the processing costs for generating a population of aggregated views. We also present a greedy algorithm for selecting redundant view element sets in order to further reduce processing costs. We demonstrate that the view element approaches perform better in terms of lower processing and storage costs than methods based on materializing views.