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Extending and Implementing Process Mining Techniques Improving Data Quality - Prevention and Mitigation

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Description

Process mining, a subfield of data science, focuses on analyzing event data recorded during process execution. Handling substantial amounts of process-related data requires robust analytical techniques. Ensuring data quality is paramount in process mining, and a pattern-based strategy is employed to systematically address various data quality issues. In this project, team will propose solution with the Odigos framework in process mining initiatives provides a systematic approach to identify the root causes of data quality problems making the steps towards preventing and mitigating data quality issues in event logs.

Team Members

1. E/18/010 Abeywickrama A.K.D.A.S. [Website, Email]
2. E/18/156 Jayathilake W.A.T.N. [Website, Email]
3. E/18/329 Sewwandi D.W.S.N. [Website, Email]

Supervisors

1. Prof. Roshan G. Ragel [Website, Email]
2. Dr. Asitha Bandaranayake [Website, Email]
3. Dr. Damayanthi Herath [Website, Email]
4. Prof. Athur ter Hofstede [Website, Email]
5. Dr. Chathura Ekanayake [Website, Email]

Links

1. Project Page
2. Github Repo
3. Department of Computer Engineering