Range sum queries in dynamic OLAP data cubes

Range sum queries play an important role in analyzing data in data cubes. Many application domains require that data cubes should be updated quite often to provide real time information. Previous techniques for range sum queries, however, can incur an update cost of O(n^d) in the worst case, where d is the number of dimensions of the data cube and n is the size of each dimension. To address this dynamic data cube problem, a technique called double relative prefix sum (DRPS), was proposed which achieves a query cost of O(n^1/3) and an update cost of O(n^d/3 ) in the worst case. The total cost of DRPS is the smallest compared with other techniques under two cost models. However, this technique causes considerable space overhead which is about n^d+dn^d-1/3. While low query cost and update cost are critical for analysis in dynamic OLAP data cubes, growing data collections increase the demand for space-efficient approaches. We propose a new technique which promises the same query cost and update cost as DRPS while the additional space requirement is only nd