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Publication date: 1 de June, 2021

KDBI – 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.


Startdate 09/09/2013
Enddate 12/09/2013
URL http://www.epia2013.uac.pt/?page_id=606