Abstract
Methods in decision analytics are becoming essential tools for organizations to process the increasing amount of collected data. At the same time, these models should be capable of representing and utilizing the tacit knowledge of experts. In other words, companies require methods that can make use of imprecise information to deliver insights in real time. In this chapter, we provide a summary of three closely related research projects designed by building on the concept of knowledge mobilization. In these three cases, we provide solutions for typical business analytical problems originating mainly form the process industry. Fuzzy ontology represented as a fuzzy relation provides the basis for every application. By looking at the similarities among the three cases, we discuss the main lessons learnt and provide some important factors to be considered in future applications of soft computing in industrial applications.
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Acknowledgments
This paper is dedicated to the memory of Péter Majlender whose important contributions to fuzzy set theory have influenced us. József Mezei acknowledges the support from the TEKES strategic research project Data to Intelligence [D2I], project number: 340/12. The research of Matteo Brunelli is supported by the Academy of Finland.
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Mezei, J., Brunelli, M. (2016). Decision Analytics and Soft Computing with Industrial Partners: A Personal Retrospective. In: Collan, M., Fedrizzi, M., Kacprzyk, J. (eds) Fuzzy Technology. Studies in Fuzziness and Soft Computing, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-319-26986-3_11
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DOI: https://doi.org/10.1007/978-3-319-26986-3_11
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