KDBI

Knowledge Discovery and Business Intelligence

The aim of this thematic track is to gather the latest research in Knowledge Discovery (KD) and Business Intelligence (BI). We encourage papers that deal with the interaction with the end users, taking into account how easily one can understand data model's representation of extracted knowledge or encode expert knowledge, as well as its impact on real organizations. In particular, papers that describe experience and lessons learned from KD/BI projects and/or present business and organizational impacts using AI technologies, are welcome.

The amount of data representing the activities of organizations that is stored in databases is exponentially growing. Moreover, business organizations are increasingly moving towards decision-making processes that are based on information. Thus, pressure to extract as much useful information as possible from these data is very strong. Knowledge Discovery (KD) is a branch of the Artificial Intelligence (AI) field that aims to extract useful and understandable high-level knowledge from complex and/or large volumes of data. Business Intelligence (BI) is an umbrella term that represents computer architectures, tools, technologies and methods to enhance managerial decision making in public and corporate enterprises, from operational to strategic level.

KD and Data Mining (DM) are faced with new challenges. The temporal and spatial nature of the data generation demands new learning approaches, since samples’ observations are no longer independent and the underlying regularities may change over time. New challenges are also to be considered when integrating background knowledge into the learning processes. Indeed, the success of hybrid models for knowledge understanding and the dead-end of several purely experimental methods in machine learning and DM are pointing to a more rationalistic view. In this context, the understanding of data and human mind emerges as crucial in combining KD with Cognitive Models. Namely, results in inductive logic or in neuro-symbolic methods seem to show the need of more knowledge aware models. Moreover, AI plays a crucial role in BI, providing methodologies to deal with prediction, optimization and adaptability to dynamic environments, in an attempt to offer support to better (more informed) decisions. In effect, several AI techniques can be used to address these problems, namely KD/DM, Evolutionary Computation and Modern Optimization, Forecasting, Neural Computing and Intelligent Agents.

Topics of Interest

• Data Analysis, including Knowledge Discovery, Data Mining, Machine Learning and Statistical Methods
• Logic and Philosophy of Scientific Discovery and its relevance to Knowledge Discovery and Business Intelligence
• Hybrid Learning Models and Methods
• Domain Knowledge Discovery (e.g. Learning from Heterogeneous, Unstructured and Multimedia data, Networks, Graphs and Link Analysis)
• Cognitive Models including Human-machine interaction for Knowledge Discovery and Management
• Classification Regression and Clustering
• Methodologies, Architectures or Computational Tools for Business Intelligence
• Artificial Intelligence applied to Business Intelligence (e.g. Knowledge Discovery, Evolutionary Computation, Intelligent Agents, Fuzzy Logic)
• Data and Knowledge Visualization
• Temporal and Spatial Knowledge Discovery
• Data Pre-Processing Techniques for Knowledge Discovery and Business Intelligence
• Bio-inspired and other cognitive related models, namely Neural Networks.
• Bayesian Learning and Inductive Logic
• Incremental Learning, Change Detection and Learning from Ubiquitous Data Streams
• Adaptive Business Intelligence
• Data Warehouse and OLAP
• Intelligent Decision Support Systems
• Learning in Neuro-Symbolic and Neural Computation Systems
• Real-word Applications (e.g. Prediction/Optimization in Finance, Marketing, Sales, Production)

Organising Committee

• Nuno Marques, New University of Lisbon, Portugal
• Paulo Cortez, University of Minho, Portugal
• João Moura Pires, New University of Lisbon, Portugal
• Luís Cavique, Univ. Aberta, Portugal
• Manuel Filipe Santos, DIS, University of Minho, Portugal
• Margarida Cardoso, ISCTE-Business School, Portugal
• Robert Stahlbock, DBE, University of Hamburg, Germany
• Zbigniew Michalewicz, SCS, University of Adelaide, Australia

Contact

nmm[at]di[.]fct[.]unl[.]pt
pcortez[at]dsi[.]uminho[.]pt

Program Committee

• Agnes Braud, Univ. Robert Schuman - Strasbourg, France
• André Ponce de Carvalho, Univ. São Paulo, Brazil
• António Abelha, University of Minho, Portugal
• Armando Mendes, Univ. Açores, Portugal
• Armando Vieira, ISEP, Portugal
• Beatriz De la Iglesia, CMP, UEA, UK
• Carlos Alzate, K.U.Leuven, ESAT/SISTA, Belgium
• Carlos Soares, University of Porto, Portugal
• Cristian Figueroa-Sepulveda, SPSS Inc, Chile
• Emilio Carrizosa, University of Sevilla, Spain
• Ernestina Menasalvas, Facultad de Informática de la Universidad Politécnica de Madrid , Spain
• Fátima Rodrigues, ISEP, Portugal
• Gregory Wheeler, University of Lisbon, Portugal
• João Gama, University of Porto, Portugal
• João Pedro Neto, University of Lisbon, Portugal
• Joaquim Ferreira da Silva, Univ. Nova de Lisboa, Portugal
• José Costa, Federal University UFRN, Brazil
• Jose Machado, University of Minho, Portugal
• Jose Neves, University of Minho, Portugal
• Logbing Cao, University of Technology Sydney, Australia
• Mário Figueiredo, IT, IST, Portugal
• Murat Caner Testik, Hacettepe University, Turkey
• Ning Chen, Instituto Politéctnico do Porto, Portugal
• Orlando Belo, Minho University, Portugal
• Pascal Hitzler, University of Karlsruhe,Germany
• Patrick Meyer, Institut Telecom/Telecom Bretagne, France
• Paulo Gomes, University of Coimbra, Portugal
• Peter Geczy, AIST, Japan
• Philippe Lenca, Institut Telecom/Telecom Bretagne, France
• Rui Camacho, Universidade do Porto, Portugal
• Stefan Lessmann, Universit of Hamburg, Germany
• Stéphane Lallich, Universit Lyon 2, France
• Theodore Trafalis, University of Oklahoma,USA
• Vasilis Aggelis, Piraeus Bank S.A., Greece
• Victor Alves, University of Minho, Portugal
• Vítor Lobo, Escola Naval, Portugal
• Wolfgang Jank, University of Maryland, USA
• Wolfram-M. Lippe, University of Muenster, Germany