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KDBI 2011 - KDBI@EPIA2011 Knowledge Discovery and Business Intelligence
Topics/Call fo Papers
Knowledge Discovery and Business Intelligence
PAULO CORTEZ (DEPARTMENT OF INFORMATION SYSTEMS, UNIVERSITY OF MINHO, PORTUGAL)
NUNO MARQUES (DEPARTAMENTO DE INFORMÁTICA, FCT-UNIVERSIDADE NOVA DE LISBOA, PORTUGAL)
LUÍS CAVIQUE (DEPARTAMENTO DE CIÊNCIAS E TECNOLOGIA, UNIVERSIDADE ABERTA, PORTUGAL)
JOÃO GAMA, LABORATORY OF ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT, UNIVERSITY OF PORTO, PORTUGAL)
MANUEL FILIPE SANTOS, DEPARTMENT OF INFORMATION SYSTEMS, UNIVERSITY OF MINHO
Due to advances in Information and Communication technologies, nowadays it is easy to collect, store, process and share data. In effect, the amount of data that is stored by organizations or individuals is exponentially growing. Moreover, business organizations are increasingly moving towards decision-making processes that are based on information. Therefore, 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. Also, 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 workshop is to gather the latest research in KD and BI. 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 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.
Topics of Interest (non exhaustive):
1. 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;
2. Business Intelligence (BI):
- 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.
3. Real-word Applications: Prediction/Optimization in Finance, Marketing, Medicine, 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. Authors should omit their names from the submitted papers, and should take reasonable care to avoid indirectly disclosing their identity.
Papers should not exceed fifteen (15) pages in length and must be formatted according to the information for LNCS authors. Papers must be submitted in PDF (Adobe's Portable Document Format) format and will not be accepted in any other format. Papers that exceed 15 pages or do not follow the LNCS guidelines risk being rejected automatically without a review. At least one author of each accepted paper must register for the conference. More information about the Springer's Lecture Notes in Computer Science (LNCS) are available on the Springer LNCS Web site.
http://www.springer.com/computer/lncs?SGWID=0-164-...
Special Issue in Journal Expert Systems:
Authors of the best papers presented at the KDBI-AT-EPIA2011 track will be invited to submit extended versions of their manuscripts for consideration in a track special issue KDBI of the 'The Wiley-Blackwell Journal Expert Systems: The Journal of Knowledge Engineering', indexed at ISI Web of Knowledge with an impact factor of 1.231: http://www3.interscience.wiley.com/journal/1179631...
Deadlines:
- Paper submission: May 10, 2011
- Acceptance notification: June 10, 2011
- Camera-ready papers: July 1, 2011
- Venue: October, 10 to 13, 2011
Organizing Committee
* Paulo Cortez, University of Minho, Portugal (contact person, http://www3.dsi.uminho.pt/pcortez)
* Nuno Marques, New University of Lisbon, Portugal (contact person, nmm-AT-di.fct.unl.pt)
* Luís Cavique, Universidade Aberta, Portugal
* João Gama, University of Porto, Portugal
* Manuel Filipe Santos, University of Minho, Portugal
Program Committee (provisory list):
- Agnes Braud, Univ. Robert Schuman - Strasbourg, France
- Albert Bifet, University of Waikato, New Zeland
- Aline Villavicencio, Universidade Federal do Rio Grande do Sul, Brasil
- Alípio Jorge, University of Porto, Portugal
- André Ponce de Carvalho, Univ. São Paulo, Brazil
- Armando Vieira, ISEP, Portugal
- Beatriz De la Iglesia, UEA, UK
- Carlos Alzate, K.U.Leuven, ESAT/SISTA, Belgium
- Carlos Soares, University of Porto, Portugal
- Elena Ikonomovska, Jozef Stekan Institute. Slovenia
- Fátima Rodrigues, ISEP, Portugal
- Hogbo Liu, Dalian Maritime University, China
- Joaquim Ferreira da Silva, Univ. Nova de Lisboa, Portugal
- José Costa, Federal University UFRN, Brazil
- Jose Machado, University of Minho, Portugal
- Logbing Cao, University of Technology Sydney, Australia
- Murat Caner Testik, Hacettepe University, Turkey
- Orlando Belo, University of Minho, Portugal
- 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
- Susana Nascimento, Univ. Nova de Lisboa, Portugal
- Theodore Trafalis, University of Oklahoma, USA
- Vítor Lobo, Escola Naval, Portugal
PAULO CORTEZ (DEPARTMENT OF INFORMATION SYSTEMS, UNIVERSITY OF MINHO, PORTUGAL)
NUNO MARQUES (DEPARTAMENTO DE INFORMÁTICA, FCT-UNIVERSIDADE NOVA DE LISBOA, PORTUGAL)
LUÍS CAVIQUE (DEPARTAMENTO DE CIÊNCIAS E TECNOLOGIA, UNIVERSIDADE ABERTA, PORTUGAL)
JOÃO GAMA, LABORATORY OF ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT, UNIVERSITY OF PORTO, PORTUGAL)
MANUEL FILIPE SANTOS, DEPARTMENT OF INFORMATION SYSTEMS, UNIVERSITY OF MINHO
Due to advances in Information and Communication technologies, nowadays it is easy to collect, store, process and share data. In effect, the amount of data that is stored by organizations or individuals is exponentially growing. Moreover, business organizations are increasingly moving towards decision-making processes that are based on information. Therefore, 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. Also, 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 workshop is to gather the latest research in KD and BI. 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 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.
Topics of Interest (non exhaustive):
1. 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;
2. Business Intelligence (BI):
- 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.
3. Real-word Applications: Prediction/Optimization in Finance, Marketing, Medicine, 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. Authors should omit their names from the submitted papers, and should take reasonable care to avoid indirectly disclosing their identity.
Papers should not exceed fifteen (15) pages in length and must be formatted according to the information for LNCS authors. Papers must be submitted in PDF (Adobe's Portable Document Format) format and will not be accepted in any other format. Papers that exceed 15 pages or do not follow the LNCS guidelines risk being rejected automatically without a review. At least one author of each accepted paper must register for the conference. More information about the Springer's Lecture Notes in Computer Science (LNCS) are available on the Springer LNCS Web site.
http://www.springer.com/computer/lncs?SGWID=0-164-...
Special Issue in Journal Expert Systems:
Authors of the best papers presented at the KDBI-AT-EPIA2011 track will be invited to submit extended versions of their manuscripts for consideration in a track special issue KDBI of the 'The Wiley-Blackwell Journal Expert Systems: The Journal of Knowledge Engineering', indexed at ISI Web of Knowledge with an impact factor of 1.231: http://www3.interscience.wiley.com/journal/1179631...
Deadlines:
- Paper submission: May 10, 2011
- Acceptance notification: June 10, 2011
- Camera-ready papers: July 1, 2011
- Venue: October, 10 to 13, 2011
Organizing Committee
* Paulo Cortez, University of Minho, Portugal (contact person, http://www3.dsi.uminho.pt/pcortez)
* Nuno Marques, New University of Lisbon, Portugal (contact person, nmm-AT-di.fct.unl.pt)
* Luís Cavique, Universidade Aberta, Portugal
* João Gama, University of Porto, Portugal
* Manuel Filipe Santos, University of Minho, Portugal
Program Committee (provisory list):
- Agnes Braud, Univ. Robert Schuman - Strasbourg, France
- Albert Bifet, University of Waikato, New Zeland
- Aline Villavicencio, Universidade Federal do Rio Grande do Sul, Brasil
- Alípio Jorge, University of Porto, Portugal
- André Ponce de Carvalho, Univ. São Paulo, Brazil
- Armando Vieira, ISEP, Portugal
- Beatriz De la Iglesia, UEA, UK
- Carlos Alzate, K.U.Leuven, ESAT/SISTA, Belgium
- Carlos Soares, University of Porto, Portugal
- Elena Ikonomovska, Jozef Stekan Institute. Slovenia
- Fátima Rodrigues, ISEP, Portugal
- Hogbo Liu, Dalian Maritime University, China
- Joaquim Ferreira da Silva, Univ. Nova de Lisboa, Portugal
- José Costa, Federal University UFRN, Brazil
- Jose Machado, University of Minho, Portugal
- Logbing Cao, University of Technology Sydney, Australia
- Murat Caner Testik, Hacettepe University, Turkey
- Orlando Belo, University of Minho, Portugal
- 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
- Susana Nascimento, Univ. Nova de Lisboa, Portugal
- Theodore Trafalis, University of Oklahoma, USA
- Vítor Lobo, Escola Naval, Portugal
Other CFPs
Last modified: 2011-04-24 16:08:44