KDBI 2013 - 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. 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 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 track 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.
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, 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
Business Intelligence (BI)/ 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-world Applications
- Prediction/Optimization in Finance, Marketing, Medicine, Sales, Production
- Mining Big Data and Cloud Computing