Fwd: [KDBI09] 1st call for papers: please distribute...
Carlos Soares
csoares at fep.up.pt
Di Feb 17 12:00:24 CET 2009
Please distribute.
Best regards,
Carlos
Begin forwarded message:
> From: Paulo Cortez <pcortez at dsi.uminho.pt>
> Date: February 17, 2009 10:33:24 AM GMT+00:00
> To: andre at icmc.usp.br, amendes at uac.pt, asv at isep.ipp.pt, bli at cmp.uea.ac.uk
> , carlos.alzate at esat.kuleuven.be, Carlos Soares
> <csoares at fep.up.pt>, cristian.figueroa at neo-metrics.com, ecarrizosa at us.es
> , emenasalvas at fi.upm.es, fr at dei.isep.ipp.pt, jgama at liacc.up.pt, jpn at di.fc.ul.pt
> , alfredo at dee.ufrn.br, lbcao at it.uts.edu.au, Luis Cavique <lcavique at univ-ab.pt
> >, mtf at lx.it.pt, mtestik at hacettepe.edu.tr, ningchen74 at yahoo.com, obelo at di.uminho.pt
> , hitzler at aifb.uni-karlsruhe.de, pgomes at dei.uc.pt,
> p.geczy at aist.go.jp, philippe.lenca at enst-bretagne.fr,
> rcamacho at fe.up.pt, lessmann at econ.uni-hamburg.de, stephane.lallich at univ-lyon2.fr
> , snt at di.fct.unl.pt, ttrafalis at ou.edu, AggelisV at winbank.gr, vlobo at isegi.unl.pt
> , wjank at rhsmith.umd.edu, lippe at math.uni-muenster.de
> Subject: [KDBI09] 1st call for papers: please distribute...
>
> Dear KDBI reviewers,
>
> Please distribute the 1st KDBI call for papers (attached below)
> among your contacts.
>
> On behalf the KDBI organizing committee.
>
> Regards,
> --
> Paulo Alexandre Ribeiro Cortez (PhD, MSc)
> Lecturer (Prof. Auxiliar) at the Department of Information Systems
> (DSI)
> University of Minho, Campus de Azurem, 4800-058 Guimaraes, Portugal
> http://www.dsi.uminho.pt/~pcortez +351253510313 Fax:+351253510300
>
>
> -----------------------------------------------------------------------------------------
> 1st 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
>
>>> Deadline for paper submission: April 15, 2009 <<
> -----------------------------------------------------------------------------------------
>
> 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
> * 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
> * Joao Gama, University of Porto, Portugal
> * Joao Pedro Neto, University of Lisbon, Portugal
> * Jose Costa, Federal University UFRN, Brazil
> * 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
> * Susana Nascimento, New Univ. of Lisbon, Portugal
> * Theodore Trafalis, University of Oklahoma,USA
> * Vasilis Aggelis, Piraeus Bank S.A., Greece
> * Vitor Lobo, Escola Naval, Portugal
> * Wolfgang Jank, University of Maryland, USA
> * Wolfram-M. Lippe, University of Muenster, Germany
> -----------------------------------------------------------------------------------------
==
Carlos Soares
Faculdade de Economia do Porto - Prof. Auxiliar
LIAAD-INESC Porto LA - Investigador
csoares at fep.up.pt
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