1. Profiling and process mining What has been done???

2. Objectives Explore what profiling techniques have been developed in PROM Investigate how these techniques can actually be applied Explore each technique in detail

3. PROM The PROM framework provides a variety of plug-ins that can be used to develop profiles. These plug-ins relate to mining or analysis of event logs. The following plug-ins are to be considered. Mining Plug-ins – Social Network Miner – Heuristics miner – Fuzzy miner – Association rule miner Analysis Plug-ins – LTL checker – Log summary – Performance sequence diagram analysis – Log clustering – Basic log statistics

4. Case Study: Philips medical systems (K.J.F.R. van Uden) Uses association rule miner plug-in to discover correlation between some proceedures LTL checker plug-in is used to validate the association rules discovered Log Clustering plug-in used to develop clusters based on the procedures that are performed within patient examinations. Clustering is based on activities and transitions. Basic Log Statistics plug-in used to show some basic statistics about the data.

5. Proposed solution Proposes a profiling solution in six building blocks – Main: Enables input based on some parameters – Filtering: provides a system which enables user to select some parameters e.g. date, region country etc. – Patient examination clustering – Process Mining – Profiling – and Visualization

6. Patient Examination Clustering Patient examinations can be clustered based on their procedures, applications or main applications. Ag- glomerative Hierarchical Clustering (AHC) algorithm because it copes with bottom-up hierarchical structure Possible to analyze all patients starting with the so-called patient examination types. Clustering based on applications

7. Process Mining Logs mined from less-structured processes, result in unstructured and hard to understand (spaghetti) process models They do not provide any meaningful abstraction from the event logs themselves, and are therefore useless to process analysts. The Fuzzy Miner is used to develop hierarchical structures thereby giving better visualization Mining is based on clusters identified

8. Profiling` User profiles of the systems in application mode are the sequences of the process instances of the selected abstraction level of the process models accompanied with the frequency of their occurrence. The frequency of the accompanying user profile is the number of process instances that have exactly the same sequence of events. User profiles are visualised as a sequence of events and constructed as a process model.

9. CLUSTERING ALGORITHMS Section 2

10. More to come