UT Institute of Computer Science Graduation Theses Registry

A Desktop Application for Advanced Business Rule Mining
Name Anti Alman
Abstract Process mining is one of the research disciplines belonging to the field of Business Process Management (BPM). The central idea of process mining is to use real process execution logs in order to discover, model, and improve business processes. There are multiple approaches to modeling processes with the most prevalent being procedural models. However, procedural models can be difficult to use in cases where the process is less structured and has a high number of different branches and exceptions. In these cases, it may be better to use declarative models, because declarative models do not aim to model the end-to-end processes step by step, but they constrain the behaviour of the process using rules thus allowing for more variability in the process model.
There are multiple applications available for working with procedural models. For example, Disco and Apromore, both of which have a highly polished user interface and are relatively easy to use. However, there are currently no comparable applications for working with declarative models.
This thesis builds on the Master’s Thesis of D. Kapisiz in order to develop an already existing application, RuM, into an accessible and easy to use process mining application. While RuM itself already has most of the needed functionality, the user interface of RuM is not well polished and does not have an appealing look in general. In this Master’s Thesis we will completely redesign and reimplement the user interface of RuM while also making technical changes in order to enable its continued development. The new user interface has been thoroughly evaluated by conducting a user evaluation involving 4 experts of declarative models and 4 experts of business process mining in general. The main findings of the user evaluation will be presented as a part of this thesis.
Graduation Thesis language English
Graduation Thesis type Master - Computer Science
Supervisor(s) Fabrizio Maria Maggi
Defence year 2020
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