Exploring OLAP aggregates with hierarchical visualization techniques

Abstract

This paper presents an approach to exploring multidimensional data cubes with hierarchical visualization techniques. Analysts interact with data in a predominantly "drill-down" fashion, i.e. from coarse grained aggregates towards the desired level of detail. We suggest that visual hierarchies are adequate for mapping the multiscale nature of decomposition as they preserve the results of the entire interaction. We introduce a class of visual structures called Enhanced Decomposition Tree. Every tree level is created by a disaggregation step along a chosen dimension, the nodes contain the corresponding sub-aggregates arranged into a chart and the edges are labeled with their dimensional values. Various layouts are proposed to account for different analysis tasks. Data cubes are queried using a schema-based browser which presents dimensions by the hierarchies of their granularity levels, thus offering an efficient way of generating hierarchical visualizations. Multiple data cubes may be explored in parallel along their shared dimensions. The power of our approach is exemplified using a real-world study from the domain of academic administration. Copyright 2007 ACM.