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: Internet expansion, real-world with increasing dynamic and unstable environments, integration of expert knowledge into the data-driven learning, and better support of informed decisions. Several AI techniques can be used to address these problems, such as Machine Learning/Data Mining/Data Science, Evolutionary Computation and Modern Optimization, Forecasting, Neural Computing and Deep Learning.
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/BI projects, presenting business or end user impacts using AI technologies, are welcome.
Topics of interest
A non-exhaustive list of topics of interest is defined as follows:
- Knowledge Discovery (KD)
- Data Pre-Processing;
- Intelligent Data Analysis;
- Temporal and Spatial KD;
- Data and Knowledge Visualization;
- Machine Learning (e.g., Decision Trees, Neural Networks and Deep
Learning, Bayesian Learning, Inductive and Fuzzy Logic);
- Hybrid Learning Models and Methods: Using KD methods and Cognitive
Models, Learning in Ontologies, inductive logic, etc.
- Domain KD: Learning from Heterogeneous, Text and Multimedia data,
Networks, Graphs and Link Analysis;
- Data Mining tasks: Classification, Regression, Clustering and
- Ubiquitous Data Mining: Distributed Data Mining, Incremental
Learning, Change Detection, Learning from Ubiquitous Data Streams;
- Business Intelligence (BI)/Business Analytics/Data Science
- Methodologies, Architectures or Computational Tools;
- Artificial Intelligence (e.g., KD, Evolutionary Computation, Intelligent Agents, Logic) 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.
All papers should be submitted in PDF format through EPIA 2020 submission Website (select “Knowledge Discovery and Business Intelligence” track): https://www.easychair.org/conferences/?conf=epia2020
Submissions must be original and can be of two types: regular (full-length) papers should not exceed twelve (12) pages in length, whereas short papers should not exceed six (6) pages. In order to assure the quality of accepted papers, full paper submissions may only be accepted as such, without requiring any paper shrinking procedures.
Each submission will be peer reviewed by at least three members of the Program Committee. The reviewing process is double blind, so authors should remove names and affiliations from the submitted papers, and must take reasonable care to assure anonymity during the review process. References to own work may be included in the paper, as long as referred to in the third person.
All accepted papers will appear in the proceedings published by Springer in the LNAI series (EPIA 2019 proceedings were indexed by the Thomson ISI Web of Knowledge, Scopus, DBLP and ACM digital library).
Special Issue of the Journal Expert Systems
Authors of the best papers presented at the KDBI 2020 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 2018 1.505).
This special issue corresponds to the 6th KDBI special issue on Expert Systems (ES) journal (e.g., 4th issue is available at: https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12314).
- Paulo Cortez, University of Minho, Portugal
- Albert Bifet, Université Paris-Saclay, France
- Luís Cavique, Universidade Aberta, Portugal
- João Gama, University of Porto, Portugal
- Nuno Marques, New University of Lisbon, Portugal
- Manuel Filipe Santos, University of Minho, Portugal
- Agnes Braud, University of Strasbourg, France
- Alberto Bugarin, University of Santiago de Compostela, Spain
- Alipio M. Jorge, University of Porto, Portugal
- Amilcar Oliveira, Universidade Aberta, Portugal
- André Carvalho, University of São Paulo, Brazil
- Antonio Tallón-Ballesteros, University of Huelva, Spain
- Armando Mendes, University of Azores, Portugal
- Carlos Ferreira, Institute of Eng. of Porto, Portugal
- Fátima Rodrigues, Institute of Eng. of Porto, Portugal
- João Moura-Pires, Univ. NOVA de Lisboa, Portugal
- Jose Alfredo Ferreira Costa, University Rio Grande Norte, Brazil
- Karin Becker, University Rio Grande Norte, Brazil
- Leandro Krug Wives, University Rio Grande Sul, Brazil
- Manuel Fernandez Delgado, University of Santiago de Compostela, Spain
- Marcos Aurélio Domingues, State University of Maringá, Brazil
- Margarida Cardoso, ISCTE-IUL, Portugal
- Mark Embrechts, Rensselaer Polytechnic Institute, USA
- Mohamed Gaber, Birmingham City University, UK
- Murat Caner Testik, Hacettepe University, Turkey
- Orlando Belo, University of Minho, Portugal
- Pedro Castillo, University of Granada, Spain
- Phillipe Lenca, IMT Atlantique, France
- Rita Ribeiro, Universidade do Porto, Portugal
- Rui Camacho, University of Porto, Portugal
- Sérgio Moro, ISCTE-IUL, Portugal
- Ying Tan, Peking University, China