Pcube: Update-eecient Online Aggregation with Progressive Feedback and Error Bounds

Data Cubes are used in large data warehouses as a tool for online aggregation of information. Typically , online aggregation is supported by specifying a range query over a multidimensional data cube. As the number of dimensions increases, supporting eecient range queries as well as updates to the data cube becomes diicult. 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 eecient querying and updating of multidimensional data cubes in large data warehouses. pCube provides intermediate results with absolute error bounds (to allow trading accuracy for fast response time), eecient updates, scalability with increasing dimensionality, and pre-aggregation to support large range queries. We present both a general solution and an implementation of pCube and report the results of experimental evaluations.