An Intelligent Approach On Data Extraction And Task Identification For Process Mining

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Lu Lu 11/28/2016 IE594 Process Mining Paper Reading Project An intelligent approach to data extraction and task identification for process mining Abstract This report is for IE594 Process Mining paper reading project. The selected paper was published in Information Systems Frontiers on December 2015. This report introduces the proposed intelligent approach of leveraging relevant process documents to data extraction and task identification from this paper. First of all, by using text mining techniques they analyzed those process documents. Results were used to identify the most relevant database tables for process mining. The key contribution of their approach is formalizing data extraction and task identification by using sequence kernel techniques. Their approach can help to reduce the effort and to increase the accuracy of data extraction and task identification for process mining. For the illustration purpose, a business expense imbursement case was used. In addition, the criticism of this study was discussed at the end. 1. Introduction Process mining has received tremendous attention in recent years. Process mining techniques allow for extracting information from event logs. It discovers models describing processes, organizations, and performance. Traditional process analyses are extremely labor-intensive and time-consuming because extracting useful information from massive event logs generated by various enterprise information system. Process mining

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