Objectives After taking this course students should be able to: have a detailed understanding of the entire process mining spectrum and the methodology for process mining analysis can derive and pre-process event logs from raw data and have understand and can work with a specialized form of event data such as event knowledge graphs, or Read More …
Many real-life phenomena studied with Data Science methods unfold over time. They often involve many people, objects, agents, machines, entities, etc. that interact with each other while distributed in time and space. Such dynamics are called processes and are present everywhere: in software systems medical treatments, logistics systems, manufacturing, and even entire organizations. Process mining Read More …
Many real-life phenomena studied with Data Science methods unfold over time. They often involve many people, objectes, agents, machines, entites, etc. that interact with each other while distributed in time and space. Such dynamics are called processes and are present everywhere: in software systems medical treatments, logistics systems, manufacturing, and even entire organizations. Process mining Read More …
Position: PhD Room: MF 7.070 Tel (internal): 6023 Links: CoursesPresentationsProjectsPublications External links: TU/e page Recent courses Recent presentations Recent projects Recent publications
Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data Read More …
Position: PhD-TA Room: MF 7.062 Tel (internal): 3173 Links: Courses Presentations Projects Publications External links: Scopus page ORCID page DBLP page TU/e page Awards Recent courses Recent presentations Recent projects Recent publications
Dirk is Associate Professor (UHD) in the PA group. He completed his PhD with summa cum laude at Humboldt-Univeristät zu Berlin and Eindhoven University of Technology in 2010. His research interests include distributed processes and systems built from distributed components for which he investigates modeling systems (using process modeling languages, Petri nets, or scenario-based techniques), Read More …
Dr. Marwan Hassani is assistant professor at the PA group with a focus on Real-Time Process Mining. His research interests include stream data mining, sequential pattern mining of multiple streams, efficient anytime clustering of big data streams and exploration of evolving graph data. He uses customer journey optimizationa and privacy-aware process mining as use cases for his Read More …
Position: UD Room: MF 7.067 Tel (internal): 4144 Links: Courses External assignments Projects Publications External links: Google Scholar page Scopus page ORCID page DBLP page TU/e page Recent courses Recent external assignments Recent projects Recent publications
Position: UD Room: MF 7.119 Tel (internal): 3425 Links: CoursesExternal assignmentsAssignmentsPresentationsProjectsPublications External links: Personal home pageGoogle scholar pageScopus pageORCID pageDBLP pageTU/e page Awards Recent courses Recent external assignments Recent assignments Recent presentations Recent projects Recent publications
Position: PhD Student Room: MF 7.062 Tel (internal): 6322 Links: Courses Presentations Projects Publications External links: Google scholar page Scopus page ORCID page DBLP page TU/e page Recent courses Recent presentations Recent projects Recent publications
