About This Special Issue
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.
Aims and Scope:
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
Real-word Applications:
- Prediction/Optimization in Finance, Marketing, Medicine, Sales, Production
- Mining Big Data and Cloud computing
- Social Network Analysis; Community detection, Influential nodes
