ASSIMILATION AND DATA FILTRATION OF CORRUGATED INDUSTRY

The increased availability of event data in the industry has growing interest in process mining. Industries and organizations are using process mining to know the operational processes which are executed. Process mining can be used to systematical ly drive innovation in a digitalized world. Process discovery approaches have problems in dealing with fine - grained event logs and less structured processes. Huge amount of data is being collected on a day - today basis pertaining to the manufacturing indust ry which in turn is updated in the information system. This unprocessed data is the motivation for the present study. The data is collected from the manufacturing industry to create and investigate the event logs. The data is maintained in the industry in various forms. The collection of data and pre - processing of it is a necessary step for building a tailor made event log; which is of a great concern in manufacturing industry. Data pre - processing converts the data into a format which will be simpler and e ffective process for the purpose of the user in order to help improve the quality of the data for further study. If no quality data, then no quality mining results. Process mining techniques facilitates the industries to produce accurate insights regarding their processes while showing only the desired traits and removing all irrelevant details. It is not easy to reproduce the best practices followed in data creation and collection in many settings due to the quality of raw data and the nature of processes. In this paper we are focusing on available data of a corrugated industry and various filtering techniques that can be used to filter the data and produce industry friendly event logs for further use by the industry.