pCube: Update-efficient online aggregation with progressive feedback and error bounds

Multidimensional data cubes are used in large data warehouses as a tool for online aggregation of information. As the number of dimensions increases, supporting efficient queries as well as updates to the data cube becomes difficult. Another problem that arises with increased dimensionality is the sparseness of the data space. In this paper we develop a new data structure referred to as the pCube (data cube for progressive querying), to support efficient querying and updating of multidimensional data cubes in large data warehouses. While the pCube concept is very general and can be applied to any type of query, we mainly focus on range queries that summarize the contents of regions of the data cube. pCube provides intermediate results with absolute error bounds (to allow trading accuracy for fast response time), efficient updates, scalability with increasing dimensionality, and pre-aggregation to support summarization of large ranges. We present both a general solution and an implementation of pCube and report the results of experimental evaluations