Introduction

In this age of big data, 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 or Metaheuristics.

Topics of interest

Research related with any aspects of the KD/BI life cycle, including: KD/BI methodologies, architectures or tools:

• Data Engineering (e.g., data integration, data preparation)
• Data Exploration (e.g., visualization, handling temporal and spatial data or data streams)
• Model Building (e.g., supervised and unsupervised models, ML algorithms, Automated ML)
• Exploitation (e.g., XAI, dashboards, predictions and decisions, monitoring, KD/BI impacts)

Interesting real-world KD/BI applications, including (among others):

• Finance
• Marketing
• Banking
• Medicine
• Education
• Industry 4.0 and 5.0
• Smart Cities and Services

Organizing committee

• Paulo Cortez, University of Minho, Portugal
• Albert Bifet, Université Paris-Saclay, FranceLuís Cavique, Universidade Aberta, Portugal
• João Gama, University of Porto, PortugalNuno Marques, New University of Lisbon, Portugal
• Manuel Filipe Santos, University of Minho, PortugalRita P. Ribeiro, University of Porto, Portugal