A study of the application of computational intelligence and machine learning techniques in business process mining

Author(s)

Ana Rocío Cárdenas Maita

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola de Artes, Ciências e Humanidades (EACH)
• Defense date: 2015-12-04

Examining board members

Marcelo Fantinato; Luciano Antonio Digiampietri; Lucinéia Heloísa Thom; Adriana Backx Noronha Viana

• Advisor: Marcelo Fantinato; Sarajane Marques Peres

Abstract

Mining process is a relatively new research area that lies between data mining and machine learning, on one hand, and business process modeling and analysis, on the other hand. Mining process aims at discovering, monitoring and improving business processes by extracting real knowledge from event logs available in process-oriented information systems. The main objective of this master\'s project was to assess the application of computational intelligence and machine learning techniques, including, for example, neural networks and support vector machines, in process mining. Since these techniques are currently widely applied in data mining tasks, it would be expected that they were also widely applied to the process mining context, which has been not evidenced in recent literature and confirmed by this work. We sought to understand the broad scenario involved in the process mining area, including the main features that have been found over the last ten years in terms of: types of process mining, data mining tasks used, and techniques applied to solving such tasks. The main focus of the study was to identify whether the computational intelligence and machine learning techniques were indeed not being widely used in process mining whereas we sought to identify the main reasons for this phenomenon. This was accomplished through a general study area, which followed scientific and systematic rigor, followed by validation of the lessons learned through an application example. This study considers various approaches to delimit the area: on the one hand, approaches, techniques, mining tasks and more commonly used tools; and, on the other hand, the publication vehicles, universities and researchers interested in the development area. The results show that 81% of current publications follow traditional approaches to data mining. The type of mining processes more study is Discovery 71% of the primary studies. These results are valuable for practitioners and researchers involved in the issue, and represent a major contribution to the area (AU)

• FAPESP's process: 13/17520-7 - Study of the Application of Intelligent Techniques in Process Mining
• Grantee: Ana Rocío Cárdenas Maita
• Support Opportunities: Scholarships in Brazil - Master