Adapting OLAP Analysis to the User’s Interest Through Virtual Cubes

Abstract

The manually performing of the operators turns OLAP analysis a tedious procedure. The huge user’s exploration space is the major reason of this problem. Most methods in the literature are proposed in the data perspective, without considering much of the users’ interests. In this paper, we adapt the OLAP analysis to the user’s interest on the data through the virtual cubes to reduce the user’s exploration space in OLAP. We first extract the user’s interest from the access history, and then we create the virtual cube accordingly. The virtual cube allows the analysts to focus their eyes only on the interesting data, while the uninteresting information is maintained in a generalized form. The Bayesian estimation was employed to model the access history. We presented the definition and the construction algorithm of virtual cubes. We proposed two new OLAP operators, through which the whole data cube can be obtained, and we also prove that no more response delay is incurred by the virtual cubes. Experiments results show the effectiveness and the efficiency of our approach.

This work is supported by the National Natural Science Foundation of China under Grant No.60473072.