Abstract
The aim of this paper is to propose and describe methodology for identification of repetitive sequences in big data sets. These repetitive sequences can represent for example sequences of failures that emerge in industrial processes. Proposed methodology deals with sequences which are based on time, when the elements of particular sequence emerged. One way to approach such identification is to use so called brute-force scanning, but this approach is very demanding on computational power and computational time for big data sets cases. Our methodology approaches this issue from the side of data mining and data analysis point of view.
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Acknowledgments
This publication was written with financial support of the KEGA agency in the frame of the project 040STU-4/2016 “Modernization of the Automatic Control Hardware course by applying the concept Industry 4.0”.
This publication is the result of implementation of the project: “UNIVERSITY SCIENTIFIC PARK: CAMPUS MTF STU - CAMBO” (ITMS: 26220220179) supported by the Research & Development Operational Program funded by the EFRR.
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Nemeth, M., Michalconok, G. (2019). Proposal of the Methodology for Identification of Repetitive Sequences in Big Data. In: Silhavy, R. (eds) Software Engineering and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 763. Springer, Cham. https://doi.org/10.1007/978-3-319-91186-1_40
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DOI: https://doi.org/10.1007/978-3-319-91186-1_40
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