Cartwright, Dyllan (2023) >_next(log): A Tool for Log Generation of Adaptive Business Processes. Bachelor's Thesis, Computing Science.
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Abstract
This thesis project focuses on the development of >_next(log), a sophisticated tool for generating logs to be used with adaptive business processes. The tool aims to provide a user-friendly and intuitive interface, streamlining the process of log generation for adaptive processes. It minimises the need for manual intervention or input by incorporating features such as a graphical user interface for creating a "Rule List", whereby users can adapt business logs by using their own custom rules. The ultimate goal is to automate the log generation process and enhance the usability of the application. Drawing inspiration from previous research done by my supervisors Arash Yadegari and Dimka Karastoyanova, this thesis leverages an approach for synthetic log generation that focuses on control-flow changes in KPI-based process adaptations. By allowing users to upload business process logs in .mxml format and corresponding business process models in .bpmn format, >_next(log) empowers users to view the uploaded models within the application. The tool's most significant feature is the ability for users to define precise sets of "Rules", which can then adapt the provided logs accordingly. The generated logs can then be used in conjunction with the ML.log thesis project by Radu Andrei Sterie to predict and identify (the injected) patterns in the logs using machine learning techniques.
| Item Type: | Thesis (Bachelor's Thesis) |
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| Supervisor name: | Karastoyanova, D. and Yadegari Ghahderijani, A. |
| Degree programme: | Computing Science |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 31 Jul 2023 11:20 |
| Last Modified: | 31 Jul 2023 11:20 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/31012 |
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