Predictive Analytics for Semi-structured Case Oriented Business Processes

1. Predictive Analytics for Semi-structured CaseOrientedBusinessProcesses Kerry Lumi Tartu 2013

2. Semi-structured CaseOrientedBusinessProcess Business process management systems typically includerestrictions such as rigid control flow and context tunneling Semi-structuredbusinessprocesseslifecycle is not fully driven by a formal process model The execution of a semi-structured process is not completely controlled by a central entity Case oriented processes are an example of semi-structuredbusinessprocesses.

3. Scenario Automobileinsuranceclaimshandlingscenario Thescenariohas been simplified for the sake of achieving clarity in our experiments and results

5. Implementation Anant-colonyoptimization (ACO) based algorithm wasappliedto create a probabilistic activitygraph from traces, and use it to identify key decision points in a given process. Usingstandard decision tree learning algorithm likelihood of different outcomes from the nodecan be correlated with the contents of documents accessed by the activitynode.

8. Conclusion Resultson an automobile insurance industry claims scenario indicate thatthisapproach can be useful for predicting outcomes that immediately followa given decision point, final outcomes, and intermediate outcomes thatoccur between immediate and final outcomes Furthermore our experiments indicatethat thisapproach can be useful for predicting outcomes of decisions insituations where not all the data values necessary to make a decision are available Thisapproach also demonstrates a way to identify decision points in a semi-structured process using a probabilistic graph without necessarily mininga process model to represent the process

9. Thankyou!