We envision a new data model for OLAP, called Intentional Analytics Model. Here, the user explores the information space by submitting intentions of information goals, i.e., why she wants to discover relevant information rather than prescriptions of what data she needs, and receives both data and annotations of highly interesting subsets of them as results. In other words, data are accompanied by knowledge insights and both of them are considered as first class citizens of the data model. Under the hood, the intentions are mapped to traditional OLAP operations and knowledge discovery algorithms. In a sense, our data model can be seen as a particular case of database exploration that takes advantage of OLAP primitives and cubes to support higher-level data analysis.

  • Intentional model

Texts and Presentations

Alexandre Chanson, Ben Crulis, Nicolas Labroche, Patrick Marcel, Verónika Peralta, Stefano Rizzi, Panos Vassiliadis. The Traveling Analyst Problem: Definition and Preliminary Study . 22nd International Workshop On Data Warehousing and OLAP (DOLAP 2020), Copenhagen, Denmark, March 30, 2020. [Online proceedings at CEUR, pp. 94-98].

P. Vassiliadis, P. Marcel, S. Rizzi. Beyond Roll-Up's and Drill-Down's: An Intentional Analytics Model to Reinvent OLAP. Information Systems, volume 85, pp. 68-91, November 2019. ISSN 0306-4379

Reference version of the intentional analytics model. The DOLAP'18 model is updated and lots of examples and explanations are given. The formal version of the intentional analytics model is available at arxiv.org, and extends the Inf. Systems paper with a full treatment of the formalities of the model. [Local folder]

Matteo Francia, Matteo Golfarelli, Patrick Marcel, Stefano Rizzi, Panos Vassiliadis. Assess Queries for Interactive Analysis of Data Cubes. 24th International Conference on Extending Database Technology (EDBT '21), March 23-26, 2021.

The ASSESS operator on evaluating the results of a query: Local folder (with PDF, mp4, ...)

Patrick Marcel, Nicolas Labroche, Panos Vassiliadis. Towards a Benefit-based Optimizer for Interactive Data Analysis. In 21st International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2019) co-located with EDBT/ICDT Joint Conference, Lisbon, Portugal, March 26, 2019.

Vision paper on optimizing data story answering [Local folder]

Panos Vassiliadis, Patrick Marcel. The road to highlights is paved with good intentions: envisioning a paradigm shift in OLAP modeling. 20th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2018), 26 March 2018, Vienna, Austria (co-located with EDBT/ICDT 2018).

First attempt for a model at DOLAP 2018. See also the local page with the paper and other material


The git for the code of the Delian Cube Engine is found here.