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

This review report explains the 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.

Running head: REVIEW REPORT
Review Report on SECPI: Searching for Explanations for Clustered Process Instances
Name of the Student
Name of the University
Author’s Note:

1
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

2
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

3
REVIEW REPORT
management is the perfect utilization of the repeatable process that helps in the improvement
of the result of the project.
This BPM or business process management helps to improve the business processes
from one end to the other by simply analyzing it properly and thus modelling the procedure
of the scenario of different scenarios for the execution of the improvements and monitoring
all the improved process for continuous optimization (Hammer 2015). Trace clustering
technique plays the most significant role in the business process management since relevant
information could be easily extracted for the purpose of perfect information utilization. For
the well structured processes such as work flows, discovery feature of the process mining has
restricted the appeal, as it is bound to confirm the fact that the prescribed features of this
process are perfect (Van der Aalst 2013). Although, most of the real life business procedures
are not at all strictly enforced by the supporting and existing information system, there are
some of the major notions of these processes.
The most significant problem or issue with the existing techniques for trace clustering
is that they are responsible for providing extremely less or almost zero insight within the
actual reasoning of the partitioning any event log in the proper method (Chang 2016). From
the model learning perspectives, this clustering bias of the technique of trace clustering helps
to determine the procedure of any solution being constructed. All of these clustering
techniques that are described within the literature of process mining have the ability in
employing the broad variety of various clustering biases. On one hand, the subset of the
techniques subsequently relies on the entire concept of distance as the proper measure of the
instance similarity (Trkman 2013). Each and every model driven technique relies on the
maximum likelihood as well as fitness optimization.

4
REVIEW REPORT
For the distance based clustering, the various techniques for data mining like
hierarchical clustering or k means are properly applied. The distance is the potential
candidate for the proper explanation of the clustering result (Becker, Kugeler and Rosemann
2013). One can easily visualize the several instances within the networked graph and thus
making use of the comparative statistical analysis of every underlying variable, which could
determine the intra cluster distance and the inter cluster distance. There is a projection of the
process instances onto the process features that would typically generate the large amount of
variables. This would eventually make the situation extremely complicated approach. To this,
it should be added that due to the large amount of variables, distance-based techniques suffer
from the curse of dimensionality problem. The conventional proximity metrics in the higher
dimensional space might not be qualitatively meaningful (De Weerdt et al. 2013). Hence, it is
argued that the entire value of the distance concept to assist the users with proper
understanding of the lower trace clustering solution. A lot of expertise is required in this
process and thus should be checked subsequently. Trace clustering for business process
management or BPM is the most effective of all and hence is termed as the best of all.
Moreover, the utilization of this particular technique is much higher than any other
techniques (Song et al. 2013). SECPI or the Searching for Explanations for Clustered Process
Instances is the most significant technique of the trace clustering technique and thus should
be utilized properly. Most of the users opt for the trace clustering as the generalization and
precision is checked in the process.
Instance Level Explanations with SECPI
This paper has described the complete new analysis approach to explain all the
differences within the clusters of the process instances (Sadiq, Soffer and Völzer 2014). The
major idea of this particular approach is that instead of providing the global explanation, a
short and precise rule are considered for every instance of individual. This is done by joining

5
REVIEW REPORT
the control flow characteristics, for the purpose to form the antecedent and cluster switch as
the consequences. The most significant objective of this technique is that accurate as well as
concise explanations are learnt with the help of this particular technique. Moreover, various
factors are determined in this process.
There are some of the major processes or steps in this specific technique of SECPI or
Searching for Explanations for Clustered Process Instances. The first and the foremost step in
this technique is the construction of the data set. At first, all the process instances are being
converted to various feature vectors. This implementation thus supports the various attribute
templates (van Oirschot et al. 2014). However, the initial experiments demonstrate that direct
follow attribute template has the ability in providing perfect explanatory power from the
perspective of control flow. The optimal configuration of this featurisation step is
investigated in this manner. Hence, the labelled set of data is properly obtained in which the
supervised techniques for data mining could be easily applied.
The second step in this technique of SECPI or Searching for Explanations for
Clustered Process Instances is properly deriving explanations from the support vector
machine or SVM classifier (Sadiq, Soffer and Völzer 2014). The approach of SECPI or
Searching for Explanations for Clustered Process Instances is eventually inspired a specific
algorithm that help to document the classifications. The most significant similarity is the
utilization of the SVM based classifier since the base model from where all the explanations
are eventually derived. As for document classification, SVMs are ideally suited in our context
because the use of multiple or complex attribute templates will quickly lead to massive
dimensionality. By proper employment of the well known large-scale linear classification
based on linear kernel SVMs, the approach could be supporting the data with several features
as well as instances (Sadiq, Soffer and Völzer 2014). The most significant contribution of this
particular paper comprises of the adaptation of the approach for the trace clustering with

6
REVIEW REPORT
various modifications. In the beginning, supporting the middle class prediction is being
developed since this context is more plausible for over two clusters. The next step is the
configuration of the algorithm where all the explanations could be limited to the behaviour
that is present within the process instance (Di Francescomarino et al. 2016). The third
significant step is that various performance optimizations had been introduced. The attributes
are avoided with zero variability and thus the prevention of the repetitive checking of the
similar attribute combinations and the proper expansion of attribute combinations is checked
and considered.
A formalized preview of the algorithm of SECPI or Searching for Explanations for
Clustered Process Instances is provided in the paper. Proper inputs are explained in this
algorithm. Various binary attributes are generated with the help of attribute templates. In the
second step of the algorithm, a classifier is trained on the set of data for any provided featured
vector and a predicted class label. After the perfect explanation of the algorithm, a proper set
of attributes are obtained (Sadiq, Soffer and Völzer 2014). All the indices set represent the
candidate explanation and thus proper interpretation is required. The SVM model with the
highest probability determines the label. SVMs remain more suitable classifier for using
within the proposed technique of SECPI in business process management.
Conclusion
Therefore, from the above review report, conclusion can be drawn that SECPI or
Search for Explanations of Clusters of Process Instances is the brand new technique that is
properly assisting the users with the proper understanding of the results of trace clustering.
This paper has perfectly explained the concept of Search for Explanations of Clusters of
Process Instances. The requirement for such a technique eventually stems from the core
observation that the specific technique for trace clustering does not give adequate insight into

7
REVIEW REPORT
the procedure that how this clustering solution is being composed. In the future work, it is
seen that there is expansion on several number of intensely related topics. In the beginning,
the impact of the attribute templates that are utilized is planned to be inspected, since they
play the most significant role in representation of the control flow domain. Moreover, the
proper investigation of the incorporation of the non control flow based attributes. The second
point is that the aggregation of the instance level explanations is the most significant research
track in this review report. The recent implementation subsequently supports the basic
investigation of various shared explanations within the instance groups. The more intelligent
visualization techniques and the rule clustering techniques are investigated in this purpose.
The various practical use cases are focused where the SECPI is proving extremely beneficial.
The significant discovery of the process model collection that is user driver is the significant
area where the feedback mechanism is supported. Moreover, the technique of SECPI has the
capability to relate to the exogenously defined clusters like the higher versus lower cost
instances and process specific control flow characteristic. The above review report has
properly provided the review analysis of the paper by the authors of Jochen De Weerdt and
Seppe vanden Broucke.

8
REVIEW REPORT
References
Becker, J., Kugeler, M. and Rosemann, M. eds., 2013. Process management: a guide for the
design of business processes. Springer Science & Business Media.
Chang, J.F., 2016. Business process management systems: strategy and implementation. CRC
Press.
De Weerdt, J., vanden Broucke, S., Vanthienen, J. and Baesens, B., 2013. Active trace
clustering for improved process discovery. IEEE Transactions on Knowledge and Data
Engineering, 25(12), pp.2708-2720.
Di Francescomarino, C., Dumas, M., Maggi, F.M. and Teinemaa, I., 2016. Clustering-based
predictive process monitoring. IEEE Transactions on Services Computing.
Dumas, M., La Rosa, M., Mendling, J. and Reijers, H.A., 2013. Fundamentals of business
process management (Vol. 1, p. 2). Heidelberg: Springer.
Ha, Q.T., Bui, H.N. and Nguyen, T.T., 2016, September. A trace clustering solution based on
using the distance graph model. In International Conference on Computational Collective
Intelligence (pp. 313-322). Springer, Cham.
Hammer, M., 2015. What is business process management?. In Handbook on Business
Process Management 1 (pp. 3-16). Springer, Berlin, Heidelberg.
Jeston, J., 2014. Business process management. Routledge.
Pan, G., Qi, G., Zhang, W., Li, S., Wu, Z. and Yang, L.T., 2013. Trace analysis and mining
for smart cities: issues, methods, and applications. IEEE Communications Magazine, 51(6),
pp.120-126.

9
REVIEW REPORT
Rosemann, M. and vom Brocke, J., 2015. The six core elements of business process
management. In Handbook on business process management 1 (pp. 105-122). Springer Berlin
Heidelberg.
Sadiq, S., Soffer, P. and Völzer, H. eds., 2014. Business Process Management: 12th
International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014,
Proceedings (Vol. 8659). Springer.
Song, M., Yang, H., Siadat, S.H. and Pechenizkiy, M., 2013. A comparative study of
dimensionality reduction techniques to enhance trace clustering performances. Expert
Systems with Applications, 40(9), pp.3722-3737.
Trkman, P., 2013. Increasing process orientation with business process management: Critical
practices’. International journal of information management, 33(1), pp.48-60.
Van der Aalst, W.M., 2013. Business process management: a comprehensive survey. ISRN
Software Engineering, 2013.
Van Der Aalst, W.M., La Rosa, M. and Santoro, F.M., 2016. Business process management.
van Oirschot, Y.P.J.M., van Dongen, B.F., Buijs, J.C.A.M. and Dijkman, R.M., 2014. Using
Trace Clustering for Configurable Process Discovery Explained by Event Log
Data (Doctoral dissertation, Master’s thesis).