User-adaptive exploration of multidimensional data

Abstract

In this paper we present a tool for enhanced exploration of OLAP data that is adaptive to a user's prior knowledge of the data. The tool continuously keeps track of the parts of the cube that a user has visited. The information in these scattered visited parts of the cube is pieced together to form a model of the user's expected values in the unvisited parts. The mathematical foundation for this modeling is provided by the classical Maximum Entropy principle. At any time, the user can query for the most surprising unvisited parts of the cube. The most surprising values are defined as those which if known to the user would bring the new expected values closest to the actual values. This process of updating the user's context based on visited parts and querying for regions to explore further continues in a loop until the user's mental model perfectly matches the actual cube. We believe and prove through experiments that such a user-in-the-loop exploration will enable much faster assimilation of all significant information in the data compared to existing manual explorations.