Phenomenon of concept drift from process mining insight

Abstract

Process mining is originated form the fact that the modern information systems systematically record and maintain history of the process which they monitor and support. Systematic study of the recorded information in process centric manner will help to understand the process in a better way. Process mining acts as enabling technology by facilitating process centric analysis of data, which other available data science like data mining etc. fails to provide. Process mining algorithms are able to provide excellent insights on the process which they analyze, but they fail to handle the change in the process. Concept drift is a phenomenon of change in the process while it is being analyzed and it is a non-stationary learning problem. As the process changes while it is being analyzed, end result of the analysis becomes obsolete. Process mining algorithms are static biased, they assume that process at the beginning of analysis period will remain as same at the end of analysis period. There is at most requirement to effectively deal with the change in process to conduct optimal analysis. The main focus of this paper is to identify different factors to be considered while designing the solution for the problem of concept drift and explain each of the identified factors briefly. As the phenomenon of concept drift is extensively under consideration for research in other scientific research disciplines, this article considers restricting the content strictly concerning to the context of process mining.