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The goal of an experimental evaluation is to applya prototype implementation of algorithms to test samples, and measure thecharacteristics of result repaired models. These results show pros and cons ofan algorithm when applied to samples with particular properties.21Chapter 4 describes selected process models, and corresponding generatedevent logs. Each sample model has been used to generate the event log with perfectfitness and other characteristics.A natural way to test or evaluate the repair technique is as follows.
Takea correct (serviceable) object , introduce artificial inconsistencies in it ( ),and then try to repair it using the evaluated/tested technique. If it works asexpected, then the repaired object should work (service) as the initial object does. Otherwise, the test is failed. The result of such a repair can be evaluatedusing suitable measuring tools. Results of different experiments may be compared.In the fourth chapter, we follow exactly the same way to evaluate themodular process model repair.
A repair technique passes the test if it is able toreconstruct the initial sample model. Besides, we applied other repair methods tocompare the results. The chapter presents tables and figures which show resultsof the process model repair using various approaches.Sample ModelEvent LogGeneratorEventLogProcess ModelRepairChangedModelRepairedModelEvaluationResultsFigure 12: Scheme of the experimental evaluationThe experiments show an applicability of our approach. The naivetechnique provides a user with perfectly fitting but significantly less precise modelscompared to those obtained by using other approaches. Moreover, the behaviour ofsome models is so diverse that the alignment-based algorithm for calculating modelprecision lacks available hardware resources. These are cases, when a repaired bythe naive technique model contains many silent transitions and transitions withoutinput/output places.22The improved method reconstructed the initial model in most cases.
Theexperimental evaluation shows its effectiveness.Figure 13 shows the model from Figure 1 repaired using the improvedtechnique. Fragments of the model changed during repair are highlighted withgreen colour. One may note that such a repair preserves structure of the initialmodel.Figure 13: Model from Figure 1 repaired using the improved techniqueWe also evaluated techniques in non-local cases.
The greedy technique iseffective when a model contains non-local inconsistencies. However, it changesa model more, than the improved technique. One can easily compare naive,improved, and greedy techniques using data shown in this chapter. Withinconsidered examples, the greedy technique constructs models, which are as preciseas models discovered from scratch.Extra large models can also be repaired using the techniques proposedin this thesis. For example, Figure 14 shows a model that contains more than athousand of nodes which has been repaired using the greedy technique. A fragmentthat has been replaced by the repair is highlighted with green.Figure 14: Extra large model repaired using the greedy technique with Inductive minerFinally, the last chapter concludes the thesis.
Main contributions of thisproject are summarized in the chapter. It also discusses open issues, and directionsfor future research on the subject of this thesis.23References1. Dumas M., van der Aalst W.M.P., ter Hofstede A.H.M.. Process-Aware Information Systems: Bridging People and Software through Process Technology.— Wiley & Sons, 2005.2. van der Aalst W.M.P..
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