EPIA 2026

Track Description

In this Information Technology age, business organizations are moving towards decision-making processes that are based on data-driven models. Knowledge Discovery (KD) is a branch of Artificial Intelligence (AI) that aims to extract useful knowledge from raw data. Business Intelligence (BI) is an umbrella term that represents computer data architectures, technologies and methods to enhance managerial decision-making. Both KD and BI are faced with new challenges, such as: Digital Transformation, Industry 4.0 and 5.0, Smart Cities, Health and other complex real-world environments. Several data-based AI techniques can be used to address these problems, such as Machine Learning (ML) and Deep Learning, Data Mining, Data Science and Business Analytics, Decision Support Systems, eXplainable AI (XAI), Artificial General Intelligence (e.g., Large Language Models) and Evolutionary Computation, Intelligent Optimization or Metaheuristics. The aim of this workshop is to gather the latest research in KD and BI. In particular, papers that describe experience and lessons learned from KD or BI projects, presenting business or end user impacts using AI technologies, are welcome.

Topics of Interest

• Research related with any aspects of the KD/BI life cycle, including: KD/BI methodologies, architectures or tools; Data Engineering; Data Exploration; Model Building; and Exploitation.
• Interesting real-world KD/BI applications, including (among others): Finance, Marketing, Banking, Medicine, Education, Industry 4.0 and 5.0, Smart Cities and Services.

Track Chairs

• Paulo Cortez - University of Minho
• Albert Bifet - Université Paris-Saclay
• Rita P. Ribeiro - University of Porto

Steering Committee

• Luís Cavique - Universidade Aberta
• João Gama - Universidade do Porto
• Nuno Marques - Universidade Nova de Lisboa
• Manuel Filipe Santos - Universidade do Minho