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

[Administrator of mailing lists of CIG]

Dear Carlos,
could you please trip the message of the personal parts 
and repost?

Thanks,

Peter Novak, Event at CIG admin.

On Fri, 10 Apr 2009 12:18:43 +0100
  Carlos Soares <csoares at fep.up.pt> wrote:
> Please, distribute
>> Carlos
>> Sent from my iPhone
>>
>> Begin forwarded message:
>>
>>> From: "Nuno C. Marques" <nmm at di.fct.unl.pt>
>>> Date: 10 de abril de 2009 10:35:15 GMT+01:00
>>> To: Joaquim Silva <jfs at di.fct.unl.pt>, ning chen 
>>><ningchen74 at gmail.com 
>>> >,  vlobo at netcabo.pt, Carlos Soares <csoares at fep.up.pt>, 
>>>Alípio Má 
>>> rio Jorge <amjorge at fep.up.pt>,  Pascal Hitzler 
>>><hitzler at aifb.uni-karlsruhe.de 
>>> >
>>> Subject: EXTENDED DEADLINE - Call for Papers: KDBI 2009 
>>>- Knowledge  
>>> Discovery and Business Intelligence
>>>
>>
>>> Dear KDBI Program Committee member,
>>>
>>> Please distribute the announce for the extended deadline 
>>>of the  
>>> KDBI 2009-Knowledge Discovery and Business Intelligence 
>>>call for  
>>> papers (sorry for any duplicate messages).
>>>
>>> Best regards,
>>> Nuno C. Marques
>>>
>>>
>>> --- 
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>>> --------------------------------------------------------------------
>>> 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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