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Enhancing Cubes with Models to Describe Multidimensional Data.

Matteo Francia1, Patrick Marcel2, Verónika Peralta2

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Summary
This summary is machine-generated.

The Intentional Analytics Model (IAM) enables users to explore data through analysis intentions, not raw data. This study implements IAM

Keywords:
Data explorationModelsMultidimensional dataOLAP

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Area of Science:

  • Data Science
  • Business Intelligence
  • Information Visualization

Background:

  • Traditional OLAP and analytics systems require users to specify data explicitly.
  • Existing systems lack intuitive methods for knowledge discovery and data exploration.

Purpose of the Study:

  • To provide a proof-of-concept for the Intentional Analytics Model (IAM) by implementing its 'describe' operator.
  • To address key research challenges in IAM, including automated model tuning and visualization.

Main Methods:

  • Developed an end-to-end implementation of the IAM 'describe' intention operator.
  • Addressed automated model size tuning (e.g., number of clusters).
  • Devised a measure for interestingness of model components and selected effective visualizations.

Main Results:

  • Successfully implemented and demonstrated the IAM 'describe' operator.
  • Validated the approach for user effort, effectiveness, efficiency, and scalability.
  • Introduced a novel visual metaphor for enhanced cube interaction.

Conclusions:

  • The implemented IAM 'describe' operator offers a viable approach to coupling OLAP and analytics.
  • This work advances knowledge-guided data exploration and visual analytics.
  • Future work can extend IAM with other intention operators and refine visualization techniques.