Process Discovery of Business Processes Using Temporal Causal Relation

Yutika Amelia Effendi, Nania Nuzulita

= http://dx.doi.org/10.20473/jisebi.5.2.183-194
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Abstract


Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.

Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.

Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.

Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.

Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.

Keywords:

Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log


Keywords


Business Process; Process Mining; Process Discovery; Temporal Causal Relation; Double Timestamp Event Log

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