Abstract
ABSTRACTThis paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.
Highlights
Introduction and related workProcess mining is a novel discipline that discovers processes as sequences of events concealed in the vast data logs [1]
The rest of the paper is structured as follows: in the second section, we give an overview of our method for process synthesis, analysis and repair, while in the third section, we introduce our use case scenario of a hotel’s data management system and describe the collected event log data used for process mining
The process models we discovered from the preprocessed log data using our process discovery method are finite state automatons, encoded as directed graphs with labelled nodes and edges in Graphviz2 dot textual format
Summary
Introduction and related workProcess mining is a novel discipline that discovers processes as sequences of events concealed in the vast data logs [1]. Process mining is used to convert vast amounts of event data to a process model. Event data are gathered from diverse sources, e.g. from websites and enterprise information systems (Internet of Content), on social networks (Internet of People) or by various interconnected machines and devices (Internet of Things). Data scientists, such as process mining experts, can use discovered process models to explore and better understand, and extract actionable knowledge from the collected event data [5]
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