Knowledge Discovery and Business Intelligence

In this age of big data, business organizations 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 complex or large volumes of data. Business Intelligence (BI) is an umbrella term that represents computer architectures, technologies and methods to enhance managerial decision-making. Both KD and BI are faced with new challenges, such as: Digital Transformation, Internet-of-Things, Industry 4.0, Smart Cities, Increasing dynamic and unstable real-world environments, explainable AI (XAI) and better support of informed decisions. Several AI techniques can be used to address these problems, such as Machine Learning and Deep Learning, Data Mining/Data Science/Business Analytics, and Evolutionary Computation and Metaheuristics.

The aim of the KDBI session is to gather the latest research in KD and BI. In particular, papers that describe experience and lessons learned from KD/BI projects, presenting business or end user impacts using AI technologies, are welcome.


The topics of interest include, but are not limited to: 

  • Knowledge Discovery (KD)
    • Data Pre-Processing
    • Temporal and Spatial KD
    • Explainable AI (XAI), Data and Knowledge Visualization
    • Machine Learning (e.g., Decision Trees, Deep Learning, Ensembles)
    • Hybrid Learning Models and Methods
    • Data Mining tasks: Classification, Regression, Clustering and Association Rules, Process Mining, Learning from Text and Multimedia data, Graph Mining
    • Data Streams and Distributed Data Mining
  • Business Intelligence (BI)/Business Analytics/Data Science
    • Methodologies, Architectures or Computational Tools
    • Artificial Intelligence (e.g., KD, Evolutionary Computation) applied to BI: Data Warehouse, OLAP, Data Mining, Decision Support Systems, Dashboards, Business Analytics, Adaptive BI and Competitive Intelligence
  • Real-word Applications
    • Finance, Marketing, Banking, Medicine, Education, Industry and Services
    • Big Data, Cloud computing, Web Intelligence and Social Network mining

Special Issue of the Journal Expert Systems

Authors of the best papers presented at the KDBI 2022 track of EPIA will be invited to submit extended versions of their manuscripts for a special issue KDBI of the The Wiley-Blackwell Journal Expert Systems: The Journal of Knowledge Engineering, indexed at ISI Web of Knowledge (ISI impact factor JCR 2020 2.587 – Q2 in Computer Science, Artificial Intelligence).

This special issue corresponds to the 7th KDBI special issue on Expert Systems (ES) journal.

Organisation Committee

  • Paulo Cortez, Department of Information Systems, University of Minho, Portugal
  • Albert Bifet, Télécom ParisTech, Université Paris-Saclay, France
  • Luís Cavique, Universidade Aberta, Portugal
  • João Gama, Laboratory of Artificial Intelligence and Decision Support, INESC TEC, University of Porto, Portugal
  • Nuno Marques, Departamento de Informática, FCT-Universidade Nova de Lisboa, Portugal
  • Manuel Filipe Santos, Department of Information Systems, University of Minho, Portugal

Program Committee

  • Agnes Braud, University Robert Schuman, France
  • Amilcar Oliveira, Universidade Aberta, Portugal
  • Armando Mendes, University of Azores, Portugal
  • Carlos Ferreira, Institute of Eng. of Porto, Portugal
  • Elaine Faria, Universidade Federal de Uberlândia, Brazil
  • Fátima Rodrigues, Institute of Eng. of Porto, Portugal
  • Fernando Bação, Universidade NOVA de Lisboa, Portugal
  • Filipe Pinto, Polytechnic Institute of Leiria, Portugal
  • José Costa, UFRN, Brazil.
  • Leandro Krug Wives, UFRGS, Brazil
  • Manuel Fernandez Delgado, University of Santiago de Compostela, Spain
  • Marcos Domingues, State University of Maringá, Brazil
  • Marcos Aurélio Domingues, State University of Maringá, Brazil
  • Margarida Cardoso, ISCTE, Portugal
  • Murate Testik, Hacettepe University, Turkey
  • Phillipe Lenca, IMT Atlantique, France
  • Rita Ribeiro, Universidade do Porto, Portugal
  • Roberto Henriques, Universidade NOVA de Lisboa, Portugal
  • Rui Camacho, University of Porto, Portugal
  • Sérgio Moro, ISCTE-IUL, Portugal