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 and Social 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 expert knowledge into the learning process. Of particular concern are business rules or cognitive models that can provide ways of intelligently handling some heavy tail events in complex natural phenomena. 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/Machine Learning, Evolutionary Computation and Modern Optimization, Forecasting, Neural Computing, Deep Learning and Intelligent Agents.
Hence, the aim of this workshop 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.
Special Issue of the Journal Expert Systems
Authors of the best papers presented at the KDBI 2015 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 JCR2013 of 0.75).
This special issue, to be edited by P. Cortez and M.F. Santos, corresponds to the 3rd KDBI special issue on Expert Systems (ES) journal. The 1st KDBI special issue is available here and the 2nd special issue is “In press”.
Topics of Interest
The main topics of interest include, but are not limited to:
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, Learning in Ontologies, inductive logic, etc.
- Domain KD: Learning from Heterogeneous, Unstructured (e.g. text) and Multimedia data, Networks, Graphs and Link Analysis);
- Data Mining and Machine Learning: Classification, Regression, Clustering and Association Rules;
- Ubiquitous Data Mining: Distributed Data Mining, Incremental Learning, Change Detection, Learning from Ubiquitous Data Streams;
Business Intelligence (BI)/Business Analytics/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.
- Prediction/Optimization in Finance, Marketing, Medicine, Sales, Production.
- Mining Big Data and Cloud computing.
- Social Network Analysis; Community detection, Influential nodes.
Submissions must be original and not published elsewhere. Papers should not exceed twelve (12) pages in length and must adhere to the formatting instructions of the conference. Each submission will be peer reviewed by at least three members of the Program 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. Acceptance will be based on the paper’s significance, technical quality, clarity, relevance and originality.
All accepted papers will be published by Springer in a volume of the LNAI-Lecture Notes in Artificial Intelligence series (indexed by the Thomson ISI Web of Knowledge). The number of pages of the accepted contributions has the following limits:
- Full Regular Papers: Contributions accepted as full papers should contain from 10 to 12 pages in its final version, according to the LNAI series formatting instructions. Extraordinarily, other two additional pages could be considered with a supplementary fee.
- Short Papers: Contributions accepted as short papers should contain from 4 to 6 pages in its final version, according to the LNAI series formatting instructions.
All accepted papers must be presented orally the conference by one of the authors and at least one author of each accepted paper must register for the conference.
Deadline for paper submission: March 23, 2015
Notification of paper acceptance: 27, April, 2015
Camera-ready papers due: 1, June, 2015
Conference dates: September 8-11, 2015
Department of Information Systems, University of Minho, Portugal
Email: pcortez (at) dsi.uminho.pt
Open University, Lisboa, Portugal
Email: lcavique (at) uab.pt
Laboratory of Artificial Intelligence and Decision Support, INESC TEC, University of Porto, Porto, Portugal
Email: jgama (at) fep.up.pt
Department of Informatics, New University of Lisbon FCT-UNL, Lisboa, Portugal
Email: nmm (at) fct.unl.pt
Manuel Filipe Santos
Department of Information Systems, University of Minho, Guimarães, Portugal
Email: mfs (at) dsi.uminho.pt
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
Elaine Faria, Federal University of Uberlandia, Brazil
Fátima Rodrigues, Institute of Eng. of Porto, Portugal
Fernando Bação, New University of Lisbon, Portugal
Filipe Pinto, Polytechnical Inst. Leiria, Portugal
Gladys Castillo, Choose Digital, USA
José Costa, UFRN, Brazil
Karin Becker, UFRGS, Brazil
Leandro Krug Wives, UFRGS, Brazil
Luis Lamb, UFRGS, Brazil
Manuel Fernandez Delgado, University of Santiago Compostela, Spain
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
Rita Ribeiro, University of Porto, Portugal
Rui Camacho, University of Porto, Portugal
Stéphane Lallich, University of Lyon 2, France
Yanchang Zhao, Australia Government
Ying Tan, Peking University, China
The KDBI organizers kindly acknowledge www.KDnuggets.com (Analytics, Data Mining, & Data Science Resources) for its support to the track.