Review Report on SECPI: Searching for Explanations for Clustered Process Instances

Running head: REVIEW REPORT
Review Report on SECPI: Searching for Explanations for Clustered Process Instances
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REVIEW REPORT
Introduction
Search for Explanations of Clusters of Process Instances or SECPI is a specific
technique, which significantly assists the users for understanding a specific trace clustering
solution (Van Der Aalst, La Rosa and Santoro 2016). This could be done by simply finding a
minimum set of control flow characteristics. The absence of these control flow characteristics
could eventually prevent the process instance from remaining within the current or recent
cluster. The shortcoming of the current trace clustering technique about the insight
provisioning into the proper computation of the partitioning is subsequently addressed by
knowing concise individual regulations, which could easily explain the reason of the certain
instance as the part of the cluster (Jeston 2014). The partitioning event logs within the several
groups of process instances are the most convenient and basic recipe to address the various
challenges for dealing with the complicated event logs. These event logs present the larger
amount of the process behaviour.
The following report reviews on the article of SECPI: Searching for Explanations for
Clustered Process Instances by authors Jochen De Weerdt and Seppe vanden Broucke. The
report will be properly reviewing this paper properly. This review report will be explaining
the entire concept of SECPI with respect to process discovery, trace clustering, user
comprehension, support vector machines and instance level explanations. Relevant
justifications are provided within the review report for understanding the entire concept
properly and perfectly.
Discussion
Trace Clustering

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REVIEW REPORT
Trace clustering can be defined as the significant approach for dealing with the
various problems that most of the event logs comprise of the extensive amount of the distinct
behaviours (Di Francescomarino et al. 2016). This distinct behaviour is the process variant,
since it enables the specific user in splitting up of log so that the various distinct models
could be eventually learnt for the purpose of describing the various underlying business
processes.
Two specific groups of collections of the approaches for trace clustering could be
easily discerned with over the one hand technique, relying on the principle of the distance
based clustering. The other hand technique helps to incorporate the particular model driven
approach (Pan et al. 2013). The very first group comprises of the various tools and techniques
that transform the input event log to any propositional format. This is done for applying all
the well known clustering techniques from the domain of data mining.
Business process management or BPM is the typical discipline within the operations
management, which utilizes the several methods for discovering, modelling, analyzing, and
measurement, improving, optimizing and finally automating the several business processes
(Dumas et al. 2013). This BPM eventually focuses on the improvement of the corporate
performance by simple management of the business processes. The proper combination of the
methods s utilized for the management of the business process of the company. All of these
business processes could be structured as well as repeatable or could be unstructured as well
as variable. There are various technologies that are enabled with the utilization of this
business process management or BPM. This type of process management is different from
the program management; since program management is concerned with the management of
the group of various inter dependent projects. On the other hand, the process management
involves program management within it (Rosemann and vom Brocke 2015). The process