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Enhancing Cubes with Models to Describe Multidimensional Data

Author

Listed:
  • Matteo Francia

    (University of Bologna)

  • Patrick Marcel

    (University of Tours)

  • Verónika Peralta

    (University of Tours)

  • Stefano Rizzi

    (University of Bologna)

Abstract

The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of describe, one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

Suggested Citation

  • Matteo Francia & Patrick Marcel & Verónika Peralta & Stefano Rizzi, 2022. "Enhancing Cubes with Models to Describe Multidimensional Data," Information Systems Frontiers, Springer, vol. 24(1), pages 31-48, February.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:1:d:10.1007_s10796-021-10147-3
    DOI: 10.1007/s10796-021-10147-3
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    References listed on IDEAS

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    1. Silvia Chiusano & Tania Cerquitelli & Robert Wrembel & Daniele Quercia, 2021. "Breakthroughs on Cross-Cutting Data Management, Data Analytics, and Applied Data Science," Information Systems Frontiers, Springer, vol. 23(1), pages 1-7, February.
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    3. David Schuff & Karen Corral & Robert D. St. Louis & Greg Schymik, 0. "Enabling self-service BI: A methodology and a case study for a model management warehouse," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
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    Cited by:

    1. Jérôme Darmont & Boris Novikov & Robert Wrembel & Ladjel Bellatreche, 2022. "Advances on Data Management and Information Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 1-10, February.

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