Abstract
Today's information systems of enterprises are incredibly complex and typically composed of a large number of participants. Running logs are a valuable source of information about the actual execution of the distributed information systems. In this paper, a top-down process mining approach is proposed to construct the structural model for a complex workflow from its multi-source and heterogeneous logs collected from its distributed environment. The discovered top-level process model is represented by an extended Petri net with abstract transitions while the obtained bottom-level process models are represented using classical Petri nets. The Petri net refinement operation is used to integrate these models (both top-level and bottom-level ones) to an integrated one for the whole complex workflow. A multi-modal transportation business process is used as a typical case to display the proposed approach. By evaluating the discovered process model in terms of different quality metrics, we argue that the proposed approach is readily applicable for real-life business scenario.
Highlights
Workflow Management Systems (WfMSs) support the execution of business processes [1] as they require the definition of processes, automate the enactment of process steps and their execution is guided by business rules and execution logic, and they record the execution steps of a business process
In contrast to the existing work [11]–[13], we explore the distributed process mining from heterogeneous logs which have the following characteristics: (1) The workflow logs used for process mining are distributed on different servers; (2) The workflow logs are recorded on different servers with different log structures; (3) The workflow logs are kept by their own organization or partner, and they are not accessible to others for security; and (4) The workflow logs of single organization can only reflect part of the business processes of the whole workflow and its interactions with other organizations
FRAMEWORK FOR TOP-DOWN PROCESS MINING A framework for top-down process mining based on Petri net refinement operation is illustrated in Fig. 4, which includes four main steps: Recording Running Logs: While a workflow system runs on several distributed servers, each server can record the running logs for each activity and store them into a log database
Summary
Workflow Management Systems (WfMSs) support the execution of business processes [1] as they require the definition of processes, automate the enactment of process steps and their execution is guided by business rules and execution logic, and they record the execution steps of a business process. Workflow logs [2], [3]–[7], contain the execution information for all instances of activities of They depict when and which actor performed which task, which contains very valuable information of the actual execution of business processes (as opposed of merely specified or desired descriptions of business processes). Q. Zeng et al.: Top-Down Process Mining From Multi-Source Running Logs Based on Refinement of Petri Nets conduct a distributed mining technique, i.e. mining process models of different organizations separately, and integrate them to obtain the whole one. Its corresponding running logs are distributed over one main server and several local servers To cope with this problem, a top-down process mining approach is proposed in our work.
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