Process Mining Book: CHAPTER 8
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The files described below are not explicitly used in chapter 8. However, the event logs they contain also contain information about resources and time. Therefore, these files can be used to apply the techniques described in Chapter 8.

The files reviewing.xes and reviewing.mxml contain an event log describing the handling of reviews for a journal. The event log consists of 100 cases (papers) and 3730 events. Each paper is sent to three different reviewers. The reviewers are invited to write a report. However, reviewers often do not respond. As a result, it is not always possible to make a decision after a first round of reviewing. If there are not enough reports, then additional reviewers are invited. This process is repeated until a final decision can be made (accept or reject). Note that this example is also used in Chapter 13 to illustrate the need for seamlessly zooming in and out (see Figures 13.6 and 13.7 show the discovered models).

Use reviewing.xes/reviewing.pnml to discover the underlying process model (already the alpha algorithm will find a good model, see for example reviewing.pnml/reviewing.tpn). Since the log also contains information about originators, contains timestamps, etc., all the process mining techniques described in the book can be applied to the event log. Start by applying the dotted chart analysis. Then use the various discovery techniques. Also discover the social network and organizational structures. Using replay, check the conformance and locate the bottlenecks.

The files teleclaim.xes and teleclaims.pnml contain an event log describing the handling of claims in an insurance company. The log contains 46138 events related to 3512 cases (claims). The process deals with the handling of inbound phone calls, whereby different types of insurance claims (household, car, etc.) are lodged over the phone. The process is supported by two separate call centers operating for two different organizational entities (Brisbane and Sydney). Both centers are similar in terms of incoming call volume and average total call handling time, but different in the way call centre agents are deployed, underlying IT systems, etc. After the initial steps in the call center, the remainder of the process is handled by the back-office of the insurance company. Although this is a synthetic event log without noise, it is difficult to mine. The alpha algorithm fails to extract the right model. The model also contains information about resources and has transactional information. Therefore, it can be used to apply the techniques discussed in Chapter 8.

Use teleclaim.xes/teleclaims.pnml to extract different process models. First apply the dotted chart analysis to understand the event log. Then use the various discovery techniques. Try to understand why most algorithms fail to discover a good process model. Also discover the social network and organizational structures. Using replay, check the conformance and locate the bottlenecks. See file teleclaim.pnml/teleclaims.tpn for an example process model.

The files repairexample.xes/repairexample.mxml and repairexamplesample2.xes/repairexamplesample2.mxml are taken from the ProM Framework Tutorial (see www.processmining.org). We refer to this tutorial for details. These logs can be used to apply most of the techniques discussed in Chapter 8.


For more example event logs we refer to www.processmining.org and http://data.3tu.nl/repository/collection:event_logs. For example, see  doi:10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54 for a real life log of a Dutch academic hospital. This event log was used for the first Business Process Intelligence Contest (BPIC 2011).
