Abstract
In this paper, we expose a hybridization methodology for helping to overcome the pitfalls of conventional What-If analysis process design and development by discovering the best recommendations for What-If analysis scenarios’ parameters using OLAP preferences. The hybridization process aims at assisting users during the decision-making processes by suggesting the most adequate scenario parameters according to their needs, making What-If scenarios more valuable, helping them during decision-making processes. The hybridization process provides several advantages to companies by making possible to study the behavior of a system without building it or creating the circumstances to make it happen in a business real-world system. Thus, knowing existing approaches for extracting preferences when dealing with OLAP application environments has clear business advantages. This work is about this, with a particular focus on discovering analytical preferences for personalizing What-If application scenarios.
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This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
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Carvalho, M., Belo, O. (2019). Discovering Analytical Preferences for Personalizing What-If Scenarios. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_35
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