Mining Logs to Model the Use of a System

Abstract

Background. Process mining is a technique to build process models from "execution logs" (i.e., events triggered by the execution of a process). State-of-the-art tools can provide process managers with different graphical representations of such models. Managers use these models to compare them with an ideal process model or to support process improvement. They typically select the representation based on their experience and knowledge of the system. Aim. This work studies how to automatically build process models representing the actual intents (or uses) of users while interacting with a software system. Such intents are expressed as a set of actions performed by a user to a system to achieve specific use goals. Method. This work applies the theory of Hidden Markov Models to mine use logs and automatically model the use of a system. Results. Unlike the models generated with process mining tools, the Hidden Markov Models automatically generated in this study provide the intents of a user and can be used to recommend managers with a faithful representation of the use of their systems. Conclusions. The automatic generation of the Hidden Markov Models can achieve a good level of accuracy in representing the actual user's intents provided the log dataset is carefully chosen. In our study, the information contained in one-month set of logs helped automatically build Hidden Markov Models with superior accuracy and similar expressiveness of the models built together with the company's stakeholder.