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BCO5501 Business Process Engineering

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BCO5501 Business Process Engineering

 BCO5501 Business Process Engineering

 

Introduction

The main purpose of this essay is to be a review of a particular research study and comparing the finding with other related research studies on the topic of trace clustering technique with the main reviewed research study.  The main research method used in this essay was analysis and discussion of the other related research study for getting the conclusion about the main research topic which is trace clustering. The research study named SECPI: Searching for Explanations for Clustered Process Instances will be reviewed in this essay. The finding of the research studies used in this essay will be discussed. In addition, the trace clustering technique will be discussed in detail from different perspectives.

There will also be a short summary which will include all the main points of the essays regarding the discussed research studies. The name of the research studies which will be compared with the main reviewed research study are Discovering Deviating Cases and Process Variants Using Trace Clustering, A Framework for Trace Clustering and Concept-drift Detection in Event Streams and Detection of Temporal Changes In Business Processes Using Cluster Techniques.

The main reviewed research study of this essay is SECPI: Searching for Explanations for Clustered Process Instances. The research study discusses the cluster techniques and how the clustering process solutions are provided and the identification of the necessary feature of control- flow set without whom the presence of a cluster of the process is not possible. The flaws of the trace cluster techniques also had been discussed in this research study. The trace clustering has been defined in this research study as a way of dealing with a problem that usually event logs have in form of a different type of behavior. These different types of behavior can also be termed as process variants of the process.

Two different types of cluster techniques have been discussed in this research study. The first type of cluster technique has been based on the main principle of distance and the second type of cluster techniques is driven by the model-based approach. The main problem stated in this research study is the difficulty in finding the real reasons for segregation of an event log in a specific manner. The trace clustering is also defined as a state of art in this research study ( De Weerdt and vanden Broucke, 2014)

The main problem statement of this research study is which is earlier stated in this essay had been discussed in detail in the research study from different perspectives. The problem of the research study is the difficulty in understanding the reason for segregation of event log in a specific manner has been discussing from a model learning perspective. This model shows that how a cluster bias of a trace clustering techniques affects the composition of the solution which is created in the process. This research study identified that a lot of cluster bias is there in the current processes.

 Another topic of this research study has focused upon is that the different techniques of clustering dependent on a different factor such as one technique depend on the distance as a factor of measurement and another technique rely on the factors like how similar two cases are and fitness optimization methods are used in the technique.

  For the distance as a measurement technique of clustering, the data mining techniques are used. In this method, the distance factor is used as a factor which helps the user understand the result of clustering. For getting finding in this method, a network graph or a statistical data used in the comparative analysis had been identified most effective. The major identified drawback of this method is the process generates a large number of variable which decreases the efficiency of the method ( De Weerdt  and vanden Broucke, 2014).

 For another type of techniques discussed in this research study which is model-driven techniques, the visual analysis of the cluster model result have been identified as the method used for analysis. The drawback identified in this method is the requirement of high level of skill in understanding and identifying the result of the analysis and identifying the impact of balancing between precision, recall, and generalization which was created by techniques of process discovery.

 

References  

De Weerdt, J. and vanden Broucke, S., 2014, September. SECPI: searching for explanations for clustered process instances. In International Conference on Business Process Management (pp. 408-415). Springer, Cham.

Hompes, B.F.A., Buijs, J.C.A.M., Van der Aalst, W.M.P., Dixit, P.M. and Buurman, J., 2015, November. Discovering deviating cases and process variants using trace clustering. In Proceedings of the 27th Benelux Conference on Artificial Intelligence (BNAIC), November (pp. 5-6).



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