[EDBT2023] Describing and Assessing Cubes Through Intentional Analytics (demo paper)
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The document describes the Intentional Analytics Model (IAM) which allows data scientists to express intentions through high-level queries to automatically enrich query results with insights. The IAM includes two operators: describe, which automatically applies machine learning models to data to rank and highlight interesting insights; and assess, which compares actual data to expected behavior by judging the outcome of comparing two cubes. The model is meant to enhance classical OLAP by providing richer query results through intentional queries.
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[EDBT2023] Describing and Assessing Cubes Through Intentional Analytics (demo paper)
- 1. EDBT 2023 Describing and Assessing Cubes Through Intentional Analytics Matteo Francia, Matteo Golfarelli, Stefano Rizzi University of Bologna, Italy 26th International Conference on Extending Database Technology (EDBT 2023)
- 2. EDBT 2023 Intentional Analytics Model (IAM) In classical OLAP… - Query multidimensional cubes through low-level operators - Query results are simple plain tables Intentional Analytics Model - Data scientists express intentions - … through high-level user-friendly syntax - … coupled with analytics to automatically enrich results with insights Two IAM operators: describe and assess Matteo Francia – University of Bologna
- 3. EDBT 2023 Describe - Automatically apply (ML) models to data - Rank insights by interest - Highlight interesting insights with sales by product, country describe revenues Research papers: - Francia, Matteo, et al. "Enhancing cubes with models to describe multidimensional data." Information Systems Frontiers 2022 Matteo Francia – University of Bologna 3 data model highlight components product type category customer gender store city country date month year quantity revenue cost SALES
- 4. EDBT 2023 Assess - Compare the actual to the expected behavior (i.e., two cubes) - Judge the outcome of the comparison with sales by country for country = ‘Italy’ assess revenues against country = ‘France’ Research papers: - Francia M, et al. "Suggesting assess queries for interactive analysis of multidimensional data." IEEE TKDE 2022. - Francia, Matteo, et al. "Assess queries for interactive analysis of data cubes." EDBT 2021. Matteo Francia – University of Bologna 4
Notas do Editor
- DIFF: [17] returns tuples that maximize difference between cells of a cube given as input Profile user exploration to recommend which unvisited parts of the cube RELAX verifies whether a pattern observed at a certain level of detail ispresent at a coarser level of detail too [19] Alternative operators have also been proposed in the Cinecubes method [7,8]. The goal of this effort is to facilitate automated reporting, given an original OLAP query as input. To achieve this purpose two operators (expressed asacts) areproposed, namely, (a) put-in-context, i.e., compare the result of the original query to query results over similar, sibling values; and (b) give-details, where drill-downs of the original query’sgroupers are performed.
- DIFF: [17] returns tuples that maximize difference between cells of a cube given as input Profile user exploration to recommend which unvisited parts of the cube RELAX verifies whether a pattern observed at a certain level of detail ispresent at a coarser level of detail too [19] Alternative operators have also been proposed in the Cinecubes method [7,8]. The goal of this effort is to facilitate automated reporting, given an original OLAP query as input. To achieve this purpose two operators (expressed asacts) areproposed, namely, (a) put-in-context, i.e., compare the result of the original query to query results over similar, sibling values; and (b) give-details, where drill-downs of the original query’sgroupers are performed.