@article {10.3844/jcssp.2019.1694.1709,
article_type = {journal},
title = {Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method},
author = {Naderifar, Vahideh and Shukur, Zarina and Sahran, Shahnorbanun},
volume = {15},
number = {11},
year = {2019},
month = {Nov},
pages = {1694-1709},
doi = {10.3844/jcssp.2019.1694.1709},
url = {https://thescipub.com/abstract/jcssp.2019.1694.1709},
abstract = {An information system is a process of collecting, processing, storing and distributing information, which leads to efficient decision-making and control in organizations. Examples of information systems include classical management systems, systems for workflow management, systems for case handling and middleware. Information systems collect information concerning important people, locations and other important matters in an organization and store relevant events in some form of structure. Based on event logs, from information systems, the discovery of process models can be made automatically by process mining techniques, without having an a priori model. By learning from the event logs, process mining aims to discover, monitor and improve processes. This paper proposes a method to discover a process model based on distributed learning automata and the condensate approach. In this proposed method, each event in the log is called a drop, which had its condensate and can be combined with other condensates. Each drop is connected to other drops and become a larger drop. All of those drops would obtain reward if it represents sequence of an event log. The evaluation results demonstrated that the proposed method could detect various patterns in the event log and discover a more efficient process model in terms of fitness, total node and total path of the mined process model.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}