EXTENDED DEADLINE - Call for Papers: KDBI 2009 - Knowledge Discovery and Business Intelligence

[Carlos Soares]

2nd Call for Papers: KDBI 2009-Knowledge Discovery and Business   
Intelligence
a thematic track of EPIA 2009, the 14th Portuguese Conference on   
Artificial Intelligence
Aveiro, Portugal, October 12-15, 2009

http://epia2009.web.ua.pt/kdbi

EXTENDED DEADLINE for paper submission: April 29, 2009 <<
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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)

Paper submission

All submissions will be refereed and selected for presentation at  the  
conference on the basis of quality and relevance to the KDBI  issues.  
A selection of high quality full papers presented in the  different  
tracks will appear in a book published by Springer, in  the LNAI  
series. All remaining papers presented at the conference will be  
published in a conference proceedings book.

Submitted papers can be full-length papers or short papers. Full   
papers can have a maximum length of 12 pages. Short papers can have  a  
maximum length of 4 pages. All papers should be prepared  according to  
the formatting instructions of Springer LNAI series (http://www.springer.com/computer/lncs?SGWID=0-164-7-72376-0 
  ). Authors should omit their names from the submitted papers, and   
should take reasonable care to avoid indirectly disclosing their   
identity.

All papers should be submitted in PDF format through the conference   
management website at:https://cmt.research.microsoft.com/EPIA2009

Organising Committee

   * Nuno Marques, New University of Lisbon, Portugal (contact  person)
   * Paulo Cortez, University of Minho, Portugal (contact person)
   * Joao Moura Pires, New University of Lisbon, Portugal
   * Luis 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

   * Andre Ponce de Carvalho, Univ. Sao Paulo, Brazil
   * Antonio Abelha, Univ. Minho, Portugal
   * Armando Mendes, Univ. Acores, 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, Neo Metrics, Chile
   * Emilio Carrizosa, University of Sevilla, Spain
   * Ernestina Menasalvas, Universidad Politecnica de Madrid, Spain
   * Fatima Rodrigues, ISEP, Portugal
   * Gregory Wheeler, New Univ. of Lisbon, Portugal
   * Joao Gama, University of Porto, Portugal
   * Joao Pedro Neto, University of Lisbon, Portugal
   * Joaquim Ferreira da Silva, New Univ. of Lisbon, Portugal
   * Jose Costa, Federal University UFRN, Brazil
   * Jose Neves, Univ. Minho, Portugal
   * Jose Machado, Univ. Minho, Portugal
   * Logbing Cao, University of Technology Sydney, Australia
   * Mario Figueiredo, IT, IST, Portugal
   * Murat Caner Testik, Hacettepe University, Turkey
   * Ning Chen, Instituto Politecnico do Porto, Portugal
   * Orlando Belo, Minho University, Portugal
   * Pascal Hitzler, University of Karlsruhe,Germany
   * Paulo Gomes, University of Coimbra, Portugal
   * Peter Geczy, AIST, Japan
   * Philippe Lenca, GET/ENST, France
   * Rui Camacho, Universidade do Porto, Portugal
   * Stefan Lessmann, Universit of Hamburg, Germany
   * Stephane Lallich, Universit Lyon 2, France
   * Theodore Trafalis, University of Oklahoma,USA
   * Vasilis Aggelis, Piraeus Bank S.A., Greece
   * Victor Alves, Univ. Minho, Portugal
   * Vitor Lobo, Escola Naval, Portugal
   * Wolfgang Jank, University of Maryland, USA
   * Wolfram-M. Lippe, University of Muenster, Germany
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