Knowledge Discovery in Databases (KDD) was defined in 1996 by Fayyad, Piatetsky-Shapiro, and Smyth as “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data".

 Since then, hundreds of data mining algorithms have been developed to assist data scientists in finding hidden knowledge in data. Business Intelligence (BI) is an increasingly popular term representing the tools and systems that play a key role in the strategic planning process of the corporation by turning knowledge into profit. Though some data mining algorithms are already being applied for BI, their knowledge discovery potential is still far from being fully utilized. 

 The fifth DM for BI conference, organized by the Department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, aims at bringing together researchers and practitioners in data mining, data science, machine learning, predictive analytics and related fields to discuss emerging trends and key issues in utilization of data mining methods for business intelligence.  

 The conference speakers include the most respected and knowledgeable data mining and business intelligence experts from Academia and Industry that will discuss the state-of-the-art and state-of-the-practice in data mining for BI, lessons learned, innovative ideas, and prospects for the future.

 
The conference is co-chaired by Prof. Bracha Shapira, Prof. Mark Last and Prof. Lior Rokach