A process-aware framework to support Process Mining from blockchain applications

Abstract

Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework’s feasibility and applicability.