Nowadays, business organizations are moving towards decision-making processes that are based on data. In parallel, advances in Information Technology have given rise to the age of big data. Thus, the pressure to extract as much useful knowledge from these data is very strong. Knowledge Discovery (KD) is a branch of Artificial Intelligence (AI) that aims to extract useful (and understandable) knowledge from complex and/or large volumes of data. Business Intelligence (BI) is an umbrella term that represents computer architectures, technologies and methods to enhance managerial decision-making. Both KD and BI face new challenges, such as: Internet expansion, real-world with increasing dynamic and unstable environments, integration of expert knowledge into the data-driven learning, and better support of informed decisions. In effect, several AI techniques can be used to address these problems, such as Machine Learning/Data Mining/Data Science, Evolutionary Computation and Modern Optimization, Forecasting, Neural Computing and Deep Learning.
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 account their impact on real organizations.
Topics of interest
A non-exhaustive list of topics of interest is defined as follows:
- 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 and Deep Learning, 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.
- Real-word Applications:
- Prediction/Optimization in Finance, Marketing, Medicine, Sales, Production.
- Mining Big Data and Cloud computing.
- Social Network Analysis; Community detection, Influential nodes.
All papers should be submitted in PDF format through EPIA 2017 submission Website (select “Knowledge Discovery and Business Intelligence” track): link to be announced soon.
Submissions must be original and can be of two types: full papers should not exceed twelve (12) pages in length, whereas short papers should not exceed six (6) pages.
Each submission will be peer reviewed by at least three members of the Programme 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.
All accepted papers will appear in the proceedings published by Springer in the LNAI series (EPIA 2015 proceedings were indexed by the Thomson ISI Web of Knowledge, Scopus, DBLP and ACM digital library).
Special Issue of the Journal Expert Systems
Authors of the best papers presented at the KDBI 2017 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 JCR2015 of 0.947).
This special issue corresponds to the 4th KDBI special issue on Expert Systems (ES) journal (e.g., the 2nd issue is available at: http://onlinelibrary.wiley.com/doi/10.1111/exsy.12087/abstract ).
Paulo Cortez, ALGORITMI/DSI, Universidade do Minho, Guimarães, Portugal
Alfred Bifet, Télécom ParisTech, Université Paris-Saclay, France
Luís Cavique, Universidade Aberta, Lisbon, Portugal
Nuno Marques, FCT-UNL, Lisbon, Portugal
Manuel Filipe Santos, ALGORITMI/DSI, Universidade do Minho, Guimarães, Portugal
More information about this track will be available soon.