A systematic mapping study of process mining. Issue 5 (28th May 2018)

• Record Type: Journal Article
• Authors: Maita, Ana Rocío Cárdenas; Martins, Lucas Corrêa; López Paz, Carlos Ramón; Rafferty, Laura; Hung, Patrick C. K.; Peres, Sarajane Marques; Fantinato, Marcelo
• Is Part Of: Enterprise information systems. Volume 12:Issue 5(2018)
• Page Start: 505
• Page End: 549
• Publication Date: 2018-05-28
• Subjects: Data mining -- machine learning -- computational intelligence -- business process management -- process mining -- systematic review; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2017.1402371
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced 'discovery' (71%) as the main type of process mining addressed and 'categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the 'graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are 'evolutionary computation' (9%) and 'decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.