Abstract
Industrial event logs, especially from low-level monitoring systems, very often have no suitable structure for process-oriented analysis techniques (i.e. process mining). Such a structure should contain three main elements for process analysis, namely: timestamp of activity, activity name and case id.
In this paper we present example data from a low-level machinery monitoring system used in underground mine, which can be used for the modelling and analysis of the mining process carried out in a longwall face. Raw data from the mentioned machinery monitoring system needs significant pre-processing due to the creation of a suitable event log for process mining purposes, because case id and activities are not given directly in the data.
In our previous works we presented a mixture of supervised and unsupervised data mining techniques as well as domain knowledge as methods for the activity/process stages discovery in the raw data. In this paper we focus on case id identification with an heuristic approach. We summarize our experiences in this area showing the problems of real industrial data sets.
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This paper presents the results of research conducted at AGH University of Science and Technology – contract no. 15.11.100.181.
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Brzychczy, E., Trzcionkowska, A. (2018). Creation of an Event Log from a Low-Level Machinery Monitoring System for Process Mining Purposes. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_7
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