Efficient aggregate queries on data cubes

Abstract: As computers are developing rapidly and become more available to the modern information society, the possibility and ability to handle large data sets in database applications increases. The demand for efficient algorithmic solutions to process huge amounts of information increases as the data sets become larger. In this thesis, we study the efficient implementation of aggregate operations on the data cube, a modern and flexible model for data warehouses. In particular, the problem of computing the k largest sum subsequences of a given sequence is investigated. An efficient algorithm for the problem is developed. Our algorithm is optimal for large values of the user-specified parameter k. Moreover, a fast in-place algorithm with good trade-off between update- and query-time, for the multidimensional orthogonal range sum problem, is presented. The problem studied is to compute the sum of the data over an orthogonal range in a multidimensional data cube. Furthermore, a fast algorithmic solution to the problem of maintaining a data structure for computing the k largest values in a requested orthogonal range of the data cube is also proposed.