KDBI – Knowledge Discovery and Business Intelligence

Nowadays, business organizations are increasingly moving towards decision-making processes that are based on information. In parallel, the amount of data representing the activities of organizations that is stored in databases is also exponentially growing. Thus, the 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. On the other hand, 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 BI are faced with new challenges. For example, due to the Internet expansion, huge amounts of data are available through the Web. Moreover, objects of analysis exist in time and space, often under dynamic and unstable environments, evolving incrementally over time. Another KD challenge is the integration of background knowledge (e.g. cognitive models or inductive logic) into the learning process. In addition, 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/Data Mining, Evolutionary Computation and Modern Optimization, Forecasting, Neural Computing and Intelligent Agents. Hence, the aim of this track is to gather the latest research in KD and BI. 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. Finally, we encourage papers that deal with the interaction with the end users, taking into its impact on real organizations.

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, Bayesian Learning, Inductive and Fuzzy Logic) and Statistical Methods
- Hybrid Learning Models and Methods: Using KD methods and Cognitive Models, Neuro-Symbolic Systems, etc.
- Domain KD: Learning from Heterogeneous, Unstructured (e.g. text) and Multimedia data, Networks, Graphs and Link Analysis
- Data Mining: Classification, Regression, Clustering and Association Rules
- Ubiquitous Data Mining: Distributed Data Mining, Incremental Learning, Change Detection, Learning from Ubiquitous Data Streams

Business Intelligence (BI)/ 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, Adaptive BI,Web Intelligence and Competitive Intelligence.

Real-world Applications

- Prediction/Optimization in Finance, Marketing, Medicine, Sales, Production
- Mining Big Data and Cloud Computing

Paper Submission

All papers should be submitted in PDF format through EPIA’2013 submission Website (select “Knowledge Discovery and Business Intelligence” track): https://www.easychair.org/conferences/?conf=epia2013. 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.

Each submission will be peer reviewed by at least three members of the Programme 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.

Papers should strictly adhere to formatting instructions of the conference: http://epia2013.uac.pt/?page_id=564. The best accepted papers will appear in the proceedings published by Springer in the LNAI series (EPIA 2011 proceedings were indexed by the Thomson ISI Web of Knowledge, Scopus, DBLP and ACM digital library). The remaining accepted papers will be published in the local proceedings with ISBN.

Special Issue in Journal Expert Systems

Authors of the best papers presented at the KDBI 2013 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 JCR2011 of 0.684, JCR2010 of 1.231): http://www3.interscience.wiley.com/journal/117963144/home

Organizing Committee

Paulo Cortez, University of Minho, Portugal
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

Program Committee

Agnes Braud, Univ. Robert Schuman, France
Albert Bifet, University of Waikato, NZ
Aline Villavicencio, UFRGS, Brazil
Alípio Jorge, University of Porto, Portugal
André Carvalho, University of São Paulo, Brazil
Armando Mendes, University of the Azores, Portugal
Bernardete Ribeiro, University of Coimbra, Portugal
Carlos Ferreira, Institute of Eng. of Porto, Portugal
Fátima Rodrigues, Institute of Eng. of Porto, Portugal
Fernando Bação, New University of Lisbon, Portugal
Filipe Pinto, Polytechnical Inst. Leiria, Portugal
Gladys Castillo, University of Aveiro, Portugal
Joaquim Silva, New University of Lisbon, Portugal
José Costa, UFRN, Brazil
Karin Becker, UFRGS, Brazil
Leandro Krug Wives, UFRGS, Brazil
Luis Lamb, UFRGS, Brazil
Marcos Domingues, University of São Paulo, Brazil
Margarida Cardoso, ISCTE-IUL, Portugal
Mark Embrechts, Rensselaer Polytechnic Institute, USA
Mohamed Gaber, University of Portsmouth, UK
Murate Testik, Hacettepe University, Turkey
Ning Chen, Institute of Eng. of Porto, Portugal
Orlando Belo, University of Minho, Portugal
Paulo Gomes, University of Coimbra, Portugal
Pedro Castillo, University of Granada, Spain
Peter Geczy, AIST, Japan
Phillipe Lenca, Telecom Bretagne, France
Rui Camacho, University of Porto, Portugal
Stefan Lessmann, University of Hamburg, Germany
Stéphane Lallich, University of Lyon 2, France
Susana Nascimento, New University of Lisbon, Portugal
Yanchang Zhao, Australia Government
Ying Tan, Peking University, China 


Paulo Cortez
Departmento de Sistemas de Informação, Universidade do Minho
4800-058 Guimarães, Portugal
Phone: 351 253 510313
Fax: 351 253 510300
e-mail: pcortez@dsi.uminho.pt


We thank the Expert Systems Journal editor-in-chief for accepting the KDBI special issue and KDnuggets for announcing this track.

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