Data Mining and Knowledge Engineering

M. S. Saravanan ¹, R. J. Rama Sree ²

Affiliations
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Rashtriya Sanskrit University, Tirupati, Andhra Pradesh, IN

Abstract

To gain competitive advantage, dyeing units are trying to streamline their processes. In order to do so, it is essential to have an accurate view of the “control flows” under consideration. In this paper, it is applied the process mining techniques to obtain meaningful knowledge about these flows that is for example to discover typical paths followed by particular group of colors. This is a non-trivial task given the dynamic nature of dyeing unit processes.
So, every process in the dyeing is stored in the form of event logs to discover the new mining model. This paper demonstrates the applicability of process mining using a real case of a shade difference process in Emerald Dyeing Unit. The process mining techniques has different mining perspectives:(1) control flow perspective, (2) organizational perspective and (3) performance perspective. In this paper, it is analyzed that how control flow perspective was useful for the dyeing process and also to discover new methods for dyeing processing. To implement these perspectives, a ProM framework is used; it is a software tool for process mining. Hence, the new mined model shows that process mining can be used to provide new insights that facilitate the improvement of existing dyeing process colouring control flows.

Keywords

Process Mining, Control Flow, Event Log, ProM Framework.

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M. S. Saravanan ¹, R. J. Rama Sree ²

Affiliations
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Rashtriya Sanskrit University, Tirupati, Andhra Pradesh, IN

Abstract

The main goal of process mining is to extract semantic knowledge from process execution traces for the purposes of process understanding, innovation and improvement. In this paper we describe a leading process model for a knowledge discovery process and metadata knowledge discovery process. Normally the knowledge can be developed or generated with various process models. Hence the new proposed process model can help to discover new decision or process method or control system for the industry experts or process controllers. Therefore, this paper deals about that how to create an event log and create a mined process model. The process mining is an emerging technology used to generate event logs from input data. Hence we demonstrated the event log for an automobile repair business process. So, the data stored in the database can be effectively analyze and discover the knowledge for various activities of automobile repair process. This paper demonstrates a metadata of an automobile repair process using process mining tools such as ProM framework by ProM mining framework Incorporation. The metadata such as Extensible Markup Language (XML) or Mining Extensible Markup Language (MXML) script is mostly accepted by process mining tools to generate a new mined process model for the better process management or discovery of knowledge for any type of applications. Hence, this paper also deals about the leading process mining algorithm and metadata standards based on XML and MXML.

Keywords

Knowledge Discovery, Process Model, Process Mining, Event Log, Meta Data.

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