From analysis to interactive exploration: Building visual hierarchies from OLAP cubes

  • Vinnik S
  • Mansmann F
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Abstract

We present a novel framework for comprehensive exploration of OLAP
data by means of user-defined dynamic hierarchical visualizations.
The multidimensional data model behind the OLAP architecture is particularly
suitable for sophisticated analysis of large data volumes. However,
the ultimate benefit of applying OLAP technology depends on the "intelligence"
and usability of visual tools available to end-users.

The explorative framework of our proposed interface consists of the
navigation structure, a selection of hierarchical visualization techniques,
and a set of interaction features. The navigation interface allows
users to pursue arbitrary disaggregation paths within single data
cubes and, more importantly, across multiple cubes. In the course
of interaction, the navigation view adapts itself to display the
chosen path and the options valid in the current context. Special
effort has been invested in handling non-trivial relationships (e.g.,
mixed granularity) within hierarchical dimensions in a way transparent
to the user.

We propose a visual structure called Enhanced Decomposition Tree to
to be used along with popular "state-of-the-art" hierarchical visualization
techniques. Each level of the tree is produced by a disaggregation
step, whereas the nodes display the specified subset of measures,
either as plain numbers or as an embedded chart. The proposed technique
enables a stepwise descent towards the desired level of detail while
preserving the history of the interaction. Aesthetic hierarchical
layout of the node-link tree ensures clear structural separation
between the analyzed values embedded in the nodes and their dimensional
characteristics which label the links. Our framework provides an
intuitive and powerful interface for exploring complex multidimensional
data sets.

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Authors

  • Svetlana Vinnik

  • Florian Mansmann

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