caa a22 4500 606228799 CHVBK 20210128101205.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s10796-015-9564-3 doi (NATIONALLICENCE)springer-10.1007/s10796-015-9564-3 An intelligent approach to data extraction and task identification for process mining [Elektronische Daten] [Jiexun Li, Harry Wang, Xue Bai] Business process mining has received increasing attention in recent years due to its ability to provide process insights by analyzing event logs generated by various enterprise information systems. A key challenge in business process mining projects is extracting process related data from massive event log databases, which requires rich domain knowledge and advanced database skills and could be very labor-intensive and overwhelming. In this paper, we propose an intelligent approach to data extraction and task identification by leveraging relevant process documents. In particular, we analyze those process documents using text mining techniques and use the results to identify the most relevant database tables for process mining. The novelty of our approach is to formalize data extraction and task identification as a problem of extracting attributes as process components, and relations among process components, using sequence kernel techniques. Our approach can reduce the effort and increase the accuracy of data extraction and task identification for process mining. A business expense imbursement case is used to illustrate our approach. Springer Science+Business Media New York, 2015 Business process management nationallicence Computational experiments nationallicence Data extraction nationallicence Process mining nationallicence Task identification nationallicence Text mining nationallicence Li Jiexun Department of Business Information Systems, College of Business, Oregon State University, 97331, Corvallis, OR, USA aut Wang Harry Department of Accounting and Management Information Systems, Lerner College of Business and Economics, University of Delaware, 19716, Newark, DE, USA aut Bai Xue Department of Operations and Information Management, School of Business, University of Connecticut, 06269, Storrs, CT, USA aut Information Systems Frontiers Springer US; http://www.springer-ny.com 17/6(2015-12-01), 1195-1208 1387-3326 17:6<1195 2015 17 10796 https://doi.org/10.1007/s10796-015-9564-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10796-015-9564-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Jiexun Department of Business Information Systems, College of Business, Oregon State University, 97331, Corvallis, OR, USA aut NATIONALLICENCE 700 1- Wang Harry Department of Accounting and Management Information Systems, Lerner College of Business and Economics, University of Delaware, 19716, Newark, DE, USA aut NATIONALLICENCE 700 1- Bai Xue Department of Operations and Information Management, School of Business, University of Connecticut, 06269, Storrs, CT, USA aut NATIONALLICENCE 773 0- Information Systems Frontiers Springer US; http://www.springer-ny.com 17/6(2015-12-01), 1195-1208 1387-3326 17:6<1195 2015 17 10796