| Title: |
Predicting Multidimensional Cubes Through Intentional Analytics |
| Authors: |
Matteo Francia; Stefano Rizzi; Matteo Golfarelli; Patrick Marcel |
| Contributors: |
Francia, Matteo; Rizzi, Stefano; Golfarelli, Matteo; Marcel, Patrick |
| Publication Year: |
2026 |
| Collection: |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
| Subject Terms: |
Data cube; OLAP; Intentional analytics model; Regress |
| Description: |
In an attempt to streamline exploratory data analysis of multidimensional cubes, the Intentional Analytics Model has been proposed as a way to unite OLAP and analytics by allowing users to indicate their analysis intentions and returning cubes enhanced with models. Five intention operators were envisioned to this end; in this work we focus on the predict operator, whose goal is to estimate the missing values of a cube measure starting from known values of the same measure or other measures using different regression models. Although prediction tasks such as forecasting and imputation are routinary for analysts, the added value of our approach is (i) to encapsulate them in a declarative, concise, natural language-like syntax; (ii) to automate the selection of the best measures to be used and the computation of the models, and (iii) to automate the evaluation of the interest of the models computed. First we propose a syntax and a semantics for predict and discuss how enhanced cubes are built by (i) predicting the missing values for a measure based on the available information via one or more models and (ii) highlighting the most interesting prediction. Then we test the operator implementation, proving that its performance is in line with the interactivity requirement of OLAP session and that accurate predictions can be returned. |
| Document Type: |
article in journal/newspaper |
| File Description: |
STAMPA |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001583025800001; volume:136; firstpage:1; lastpage:16; numberofpages:16; journal:INFORMATION SYSTEMS; https://hdl.handle.net/11585/1025212; https://www.sciencedirect.com/science/article/pii/S0306437925001140 |
| DOI: |
10.1016/j.is.2025.102628 |
| Availability: |
https://hdl.handle.net/11585/1025212; https://doi.org/10.1016/j.is.2025.102628; https://www.sciencedirect.com/science/article/pii/S0306437925001140 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY) ; license uri:iris.PUB15 |
| Accession Number: |
edsbas.34FE1BAB |
| Database: |
BASE |