Future trends in business analytics and optimization

Article type: Research Article

Authors: Brown, Donald E.^a | Famili, Fazel^b | Paass, Gerhard^c | Smith-Miles, Kate^d | Thomas, Lyn C.^e | Weber, Richard^{f; *} | Baeza-Yates, Ricardo^g | Bravo, Cristián^f | L'Huillier, Gaston^h | Maldonado, Sebastiánⁱ

Affiliations: [a] Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA | [b] National Research Council of Canada, Ottawa, Canada | [c] Fraunhofer IAIS, Germany | [d] School of Mathematical Sciences, Monash University, Wellington Road, Clayton, Victoria, Australia | [e] School of Management, University of Southampton, Southampton, UK | [f] Department of Industrial Engineering, Universidad de Chile, República, Santiago, Chile | [g] Yahoo! Research, Barcelona, Spain | [h] Groupon Inc., Palo Alto, CA, USA | [i] Faculty of Engineering and Applied Sciences, Universidad de los Andes, Las Condes, Santiago, Chile

Correspondence: [*] Corresponding author: Richard Weber, Department of Industrial Engineering, University of Chile, República 701, Santiago, Chile. E-mail: [email protected].

Abstract: During the last decades, the disciplines of Data Mining and Operations Research have been working mostly independent of each other. However, the increasing complexity of today's applications in areas such as business, medicine, and science requires more and more interaction between both disciplines. On the one hand, several data mining algorithms are based on optimization methods. On the other hand, in several applications the pure Knowledge Discovery in Databases (KDD) process is not sufficient since it does not take explicitly into account the entire decision process. This report presents future trends in Business Analytics and Optimization discussed at the panel sessions during the workshop on Business Analytics and Optimization (BAO'2010).

Keywords: Future trends, Business Analytics and Optimization, Data Mining, Methodological Research, Real-life Applications

DOI: 10.3233/IDA-2011-0506

Journal: Intelligent Data Analysis, vol. 15, no. 6, pp. 1001-1017, 2011