Authors:
André Cristiano Kalsing
1
;
Cirano Iochpe
1
;
Lucinéia Heloisa Thom
1
and
Gleison Samuel do Nascimento
2
Affiliations:
1
Federal University of Rio Grande do Sul, Brazil
;
2
Universidade Federal do Rio Grande do Sul, Brazil
Keyword(s):
Evolutionary Learning, Process Mining, Incremental Process Mining, Legacy Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Legacy Systems
;
Modeling of Distributed Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
Abstract:
Incremental Process Mining is a recent research area that brings flexibility and agility to discover process models from legacy systems. Some algorithms have been proposed to perform incremental mining of process models. However, these algorithms do not provide all aspects of evolutionary learning, such as update and exclusion of elements from a process model. This happens when updates in the process definition occur, forcing a model already discovered to be refreshed. This paper presents new techniques to perform incremental mining of execution logs. It enables the discovery of changes in the process instances, keeping the discovered process model synchronized with the process being executed. Discovery results can be used in various ways by business analysts and software architects, e.g. documentation of legacy systems or for re-engineering purposes.