Exploration and visualization of OLAP cubes with statistical tests

ABSTRACT

In On-Line Analytical Processing (OLAP), users explore a database cube with roll-up and drill-down operations in order to find interesting results. Most approaches rely on simple aggregations and value comparisons in order to validate findings. In this work, we propose to combine OLAP dimension lattice traversal and statistical tests to discover significant metric differences between highly similar groups. A parametric statistical test allows pair-wise comparison of neighboring cells in cuboids, providing statistical evidence about the validity of findings. We introduce a two-dimensional checkerboard visualization of the cube that allows interactive exploration to understand significant measure differences between two cuboids differing in one dimension along with associated image data. Our system is tightly integrated into a relational DBMS, by dynamically generating SQL code, which incorporates several optimizations to efficiently explore the cube, to visualize discovered cell pairs and to view associated images. We present an experimental evaluation with medical data sets focusing on finding significant relationships between risk factors and disease.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.6 Methodology and Techniques
          Subjects: Interaction techniques

General Terms:
Algorithms, Human Factors, Performance

Keywords:
cube, parametric test, visualization