An evolutionary approach to the discovery of unstructured business processes based on cooperative coevolution and the island model

• Grant number: 17/26491-1
• Support Opportunities: Scholarships abroad - Research
• Start date: August 01, 2018
• End date: July 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Marcelo Fantinato
• Grantee: Marcelo Fantinato
• Host Investigator: Hajo Alexander Reijers
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: University Amsterdam (VU), Netherlands

Abstract

The combination of Business Process Management (BPM) and data mining has established a new research field -- known as process mining. The goal of process mining is to extract knowledge about data obtained from the work carried out at different stages of the BPM life-cycle. Process mining seeks to improve business processes by discovering links between variables and behavioral (or misbehavioral) patterns. The data to be mined are usually formed of event logs produced by the information systems used by organizations. Although there has already been a significant evolution regarding the specific techniques required for process mining, they are still unsuitable for unstructured processes, which are in fact those most often found in real organizations. The execution flow of unstructured processes has a weak causal dependence on its activities, i.e., these flows largely depend on occasional decisions made by their participants, which makes the execution of the instances essentially different from each other. This high degree of irregular behavior leads to considerable complexity and represents a challenge for current process mining techniques. Some studies have sought the support of advanced data mining techniques to assist in handling this type of scenario, including the use of genetic algorithms. However, even with the aid of genetic algorithms, the problem of how to discover unstructured business process models has not yet been satisfactorily resolved. This project adopts two advanced strategies: cooperative coevolution and the island model. Cooperative coevolution makes a subjective fitness assessment of individuals by determining whether or not they work well together; the island model gives rise to the evolution of subpopulations. It is expected that better solutions will be found for the discovery of unstructured process models.

Scientific publications

(8)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

AVILA, DIEGO TORALES; CIGANA, RAPHAEL PIEGAS; FANTINATO, MARCELO; REIJERS, HAJO A.; MENDLING, JAN; THOM, LUCINEIA HELOISA; HARTMANN, S; KUNG, J; CHAKRAVARTHY, S; ANDERSTKOTSIS, G; et al. An Experiment to Analyze the Use of Process Modeling Guidelines to Create High-Quality Process Models. DATABASE AND EXPERT SYSTEMS APPLICATIONS, PT II, v. 11707, p. 11-pg., 2019-01-01. (17/26491-1)

NEUBAUER, THAIS RODRIGUES; PERES, SARAJANE MARQUES; FANTINATO, MARCELO; LU, XIXI; REIJERS, HAJO ALEXANDER. Interactive clustering: a scoping review. ARTIFICIAL INTELLIGENCE REVIEW, v. 54, n. 4, p. 2765-2826, APR 2021. (17/26487-4, 17/26491-1)

BIAZUS, MILLER; DOS SANTOS, CARLOS HABEKOST; TAKEDA, LARISSA NARUMI; DE OLIVEIRA, JOSE PALAZZO MOREIRA; FANTINATO, MARCELO; MENDLING, JAN; THOM, LUCINEIA HELOISA; HARTMANN, S; KUNG, J; CHAKRAVARTHY, S; et al. Software Resource Recommendation for Process Execution Based on the Organization's Profile. DATABASE AND EXPERT SYSTEMS APPLICATIONS, PT II, v. 11707, p. 11-pg., 2019-01-01. (17/26491-1)

CASTRO, CAMILA F.; FANTINATO, MARCELO; AKSU, UNAL; REIJERS, HAJO A.; THOM, LUCINEIA H.; FILIPE, J; SMIALEK, M; BRODSKY, A; HAMMOUDI, S. Towards a Conceptual Framework for Decomposing Non-functional Requirements of Business Process into Quality of Service Attributes. PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS 2019), VOL 2, v. N/A, p. 12-pg., 2019-01-01. (17/26491-1, 17/26487-4)

GARCIA, MARCIA TAVARES; NUNES, MARINA MACEDO; FANTINATO, MARCELO; PERES, SARAJANE MARQUES; THOM, LUCINEIA HELOISA. BPMN-Sim: A multilevel structural similarity technique for BPMN process models. INFORMATION SYSTEMS, v. 116, p. 26-pg., 2023-04-13. (17/26491-1)

FANTINATO, MARCELO; PERES, SARAJANE MARQUES; REIJERS, HAJO A.. X-Processes: Process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm. INFORMATION SYSTEMS, v. 119, p. 25-pg., 2023-07-10. (20/05248-4, 17/26491-1)

FANTINATO, MARCELO; PERES, SARAJANE MARQUES; REIJERS, HAJO A.; IEEE COMP SOC. X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method. 2021 IEEE 25TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE (EDOC 2021), v. N/A, p. 10-pg., 2021-01-01. (17/26491-1, 20/05248-4)

BORGES, EVANDO S.; FANTINATO, MARCELO; AKSU, UNAL; REIJERS, HAJO A.; THOM, LUCINEIA H.; FILIPE, J; SMIALEK, M; BRODSKY, A; HAMMOUDI, S. Monitoring of Non-functional Requirements of Business Processes based on Quality of Service Attributes of Web Services. PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS 2019), VOL 2, v. N/A, p. 8-pg., 2019-01-01. (17/26491-1, 17/26487-4)