In-depth Review: Mining Project-Oriented Business Processes Article

This literature review critically examines the article 'Mining Project-Oriented Business Processes' by Bala et al., focusing on methodologies for improving performance and compliance in business process management. The review compares the article's findings with those of Van der Aalst et al. and Rebuge and Ferreira, highlighting the importance of data analysis and organized output in process management. The study emphasizes the development of algorithms for efficient data organization, referencing the use of version control systems and the challenges of managing unstructured data. It further explores the application of process mining in healthcare, noting the dynamic nature of business and healthcare processes and the necessity for automatic mechanisms to ensure homogeneity. The review concludes that effective process mining algorithms are crucial for managers to analyze and monitor processes, advocating for advancements that capture and record the time and actor responsible for process execution.

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ARTICLE REVIEW
“Mining Project-Oriented Business Processes”
by Saimir Bala, Cristina Cabanillas, Andreas Solti and Jan Mendling
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Article Review
Introduction
This research essay attempts to review an article “Mining Project-Oriented Business
Processes” published in 2015 by Springer International Publishing. The essay endeavours in a
discussion of the findings realized by the authors of the aforementioned article. This paper’s
primary purpose is to scrutinize and analyze the methodologies used in business process
management as illustrated in “Mining Project-Oriented Business Processes” and other two
articles covering the same topic. The paper concurs with the articles’ bottom line which
suggests that proper business process management demands improvement of performance
and compliance in different stages and processes. The method of data collection employed in
the essay is a case study as discussions and findings in a few articles are critically analyzed.
In the article review, a detailed, comprehensive and scrupulous analysis will be done on the
articles. The paper will also compare and contrast the information gathered in our primary
article with the other two articles.The review borrows largely from two articles: “Business
Process Mining: An Industrial Application” by Van der Aalst et al. and “Business Process
Analysis in Healthcare Environments: A Methodology based on Process Mining” by Rebuge
and Ferreira (2012). The three articles unanimously assert that efficient process organization
requires simplicity in the analysis of data within the processes carried out.
Mining Project-Oriented Business Processes (Overview)
Bala et al. (2015) embark in a research project that attempts to discover new effective
methodologies that will help to improve the performance and compliance techniques used in
the various processes in the field of process management. These scholars come up with an
algorithm that would help managers comfortably monitor and analyze pieces of data. The
scholar’s major objective is to suggest a working discovery technique that would provide
output in an organized way. The scholars identify the problems faced in business process
management by giving a case example of the overly used version control system (VCS).
Despite process mining techniques of the VCS providing relevant perspectives on event data,
the technique fail to provide output that is readily organized for managers to monitor and
analyze (Bala et al. 2015, p. 1). The article tries to come up with an algorithm that creates
output models which visualizes work history in firms. The article employed an experimental
research design where the algorithm was compared and contrasted with other approaches
(Bala et al. 2015, p. 10). The algorithm was initiated and coded using JAVA programming

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language and the software can be run in computer machine specified Core i-5 Intel R with A
random access memory of 15.6 GB. The programme can also run in a 64 bit Linux kernel
3.13 (Bala et al. 2015, p. 11).
According to Bala at al. (2015, p. 1), business process management becomes a
difficult task especially when data does not flow from one centralized engine. The scholars
argue that in spite of data being available in version control systems (VCS), it is strenious to
track the specific actions done by different people throughout the processes. It is difficult for
managers and supervisors to track and monitor the progress of unstructured pieces of
information from different processes. The question of when and how a certain process was
executed is fundamental in management of business activities and processes. The scholars
attempt to provide an effective remedy to the discipline of data and process management by
designing an algorithm which ensures that data pieces flowing from different sources of a
firm are accurately and readily organized. By using an automatic algorithm termed as ‘project
mining’ by the scholars, data would be readily organized and thus easily analysed (Bala et al.
2015, p. 2). The algorithm has an advantage because its creators ensured that its output is
arranged as per the exact structure in project-oriented business processes (Bala et al. 2015, p.
2). This would save time for analysts, managers and any other relevant stakeholder who may
wish to inspect the processes of any firm.
Apart from having ‘event logs’, business firms and organizations are eased need to
monitor their business processes (Van der Aalst 2007, p. 714). These logs track the history
and the transactions in a business setting. The authors hold that proper business process
management requires effective mining of data. Van der Aalst and his contemporaries analyze
event logs such as WfM, ERP, CRM, SRM and B2B just as Bala and his contemporaries
critically scrutinized the VCS history logs. Van der Aalst et al (2007, p. 713) employed a case
study on the Dutch National Public Works Department to test data mining methodologies on
three different angles such as the process, organization and case perspectives. Each of these
perspectives reviews the topic of mining at a different standpoint in the industrial
environment. The authors’ aim was to demonstrate the relevance of their algorithm in mining
of data and processes in the industrial setting.
Both of Bala et al.’s and Van der Aalst et al.’s articles imply to explicitly suggest
working algorithms which may be beneficial in improving mining and management practices
in the business and industrial settings respectively. Business process mining aims at devising
automatic models which describe explain and clarify the behaviours as seen from the event

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logging systems (Van der Aalst 2007, p. 714). Moreover, the algorithms put forward have a
characteristic property of holding some assumptions. For instance, the algorithm devised by
Bala and his contemporaries assumes that the organizational data storage portals shows the
systematic arrangement of the assignment, that all organizational packages are available in a
certain directory and that the staff involved in the business processes commit their job
regularly during the scheduled active working times (Bala et al. 2015, p. 15). In addition, Van
der Aalst and his companions assert that their algorithm assumes that the machines running
the algorithm are located in quiet areas where there is no noise from the surroundings and that
there are no exceptions (Van der Aalst et al. 2007, p. 714).
In any firm, the flow of data is fundamental. Information regarding to data capturing,
recording and execution is not only of use to an analysts but also to supervisors, auditors and
managers. The transactions and history are stored in event logs. Some events may have a
‘timestamp’ which indicates the time a certain transaction, process or execution occurred
(Van der Aalst 2007, p. 714). The scholars further assert in cases where people are involved,
event logs may record the name of the person executing the process or event. This is evident
in our supermarket businesses where the name of the cashier who served us is recorded in our
receipts. The time when the transaction was finished is also recorded. The sentiments held in
the article by Bala et al (2015) seem to suggest that event logs provide disorderly output
which may pose difficulty to the manager. Bala and his contemporaries had looked singularly
at the VCS and assumed that event logs hardly present any readily organized data. However,
as Van der Aalst and his contemporaries had earlier asserted, some of these event logs are
well organized, with dates, time and the name of the person executing the event recorded.
The two articles’ opinions thus seem to differ.
Rebuge and Ferreira (2012) also agree that there are numerous advantages realized in
establishing process mining in the medical environment. Due to the intricacy and the multi-
faceted nature of the healthcare sector, process mining appears to be a working methodology
to obtain, analyze, monitor and understand the many event logs present in healthcare
institutions (Rebuge and Ferreira 2012, p. 1). Employment of working process mining
techniques helps in the management of the ceaseless workflows in hospitals and health
centres. Use of improper, ineffective and inefficient healthcare processes is one of reasons
behind the many technical mistakes committed by our health practitioners (Rebuge and
Ferreira 2012, p. 1). Proper and working mining processes are fundamental in such a
sensitive sector so as to improve on healthcare services which will eventually save lives. The

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scholars recommend that information systems in healthcare institutions should be devised in
such a way that they direct, support, integrate and coordinate the clinical and the
administrative processes (Rebuge and Ferreira 2012, p. 2). The scholars suggest that efficient
process mining may help in streamlining the execution and interpretation in both the medical
treatment processes and the generic organizational processes (Rebuge and Ferreira 2012, p.
2). The article of the scholars was based on a case study done in the Hospital of Sao
Sebastiao, Portugal.
Rebuge and Ferreira (2012) sentiment that some environments involve too many
actors and thus there need be a system which regulates the actions of these actors was
confirmed by Bala et al. (2015). In a healthcare environment, there is a wide distribution of
human collaboration and the patients, sometimes, depend on the autonomy and the decisions
made by any of the actors (Rebuge and Ferreira 2012, p. 3). In a business setting, processes
depend on various actors who normally execute their actions and document their work in a
semi-structured way (Bala et al. 2015, p. 2). Moreover, the two groups of scholars imply that
business processes are dynamic. Business processes are dynamic because they are executed
according to their specific needs of a particular project (Bala et al. 2015, p. 2). On the other
hand, healthcare processes regularly change due to new administrative orders, technological
advancements and discovery of new drugs (Rebuge and Ferreira 2012, p. 2). The two articles
admit that mining processes are relevant in business and healthcare process management so
as to curb these challenges. The fact that any organization is composed of many individuals
with different understanding, intellect, temperament, tolerance and dexterity makes it almost
impossible for any organization to provide homogeneous goods or services. Some
employees’ commodities may always be better that the others- sometimes, two employees
may produce commodities whose quality is the extreme opposite of the others. In such cases,
there need to be an automatic mechanism, scarcely human, to ensure homogeneity in the
processes involved in any setting. Such automatic is an algorithm discussed by the
aforementioned scholars. Process mining is an interesting remedy to mitigate such problems
(Rebuge and Ferreira 2012, p. 4). Process mining is successful especially when applied in the
analyses of healthcare processes especially in workflow management, case handling, and
hospital information systems (Mans et al. 2008, p. 426).
One overt difference between Rebuge and Ferreira’s article and Bala et al.’s article is
on the settings in which the authors try to identify proper process mining mechanisms. While

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Bala et al.’s article tried to explain on the need of the mining algorithm in a business
perspective, Rebuge and Ferreira’s article focus on employing an algorithm that dictates the
processes in a healthcare system. Although the articles emphasize on the need of having
process mining techniques, the techniques used applicability may not be universal. Despite
being a process mining algorithm, the algorithm designed in Bala et al.’s article could be
useless when applied in a hospital setting. Moreover, the process mining algorithm suggested
by Rebuge and Ferreira (2012) may fail to function in a business setting. Moreover, an
algorithm in one setting may fail in another. The application of these algorithms is hardly
seen in real life business processes (Van der Aalst et al. 2007, p. 713).
Conclusion
To conclude, proper business process management calls for the employment of an
effective and efficient process mining algorithms. Process mining makes it easy for managers
to analyze and monitor the transactions, events and processes executed by their juniors.
Automatic process mining techniques should be advanced in such a way that they capture and
record the time and the name of the actor responsible for any process execution. Moreover,
the use of process mining mechanisms such as algorithms aids to curb the problems such as
process dynamics and heterogeneity of commodities. Due to the fact that algorithms are
automated, they streamline the processes such that each process indistinguishably resembles
the other. Human weaknesses while executing processes are thus likely to be solved by the
employment of such algorithms. The discussed process mining algorithms, however, have
been found to suffer from the assumptions. For instance, in one of the algorithms, workers
have been assumed to commit to their work and to have executed their processes during work
time. In another algorithm, the algorithm has been assumed to only work in non-noisy area.
Apart from the setbacks seen in the assumptions, process mining algorithms are rarely used in
real life situations. This study suggests that there should be more research done in the field of
business process management focussing on effacing the existing difficulties in establishing
effective and efficient process mining algorithms.

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References
Bala, S., Cabanillas, C., Mendling, J., Rogge-Solti, A., and Polleres, A. (2015). Mining
project-oriented business processes. In Business Process Management. Lecture Notes in
Computer Science, 9253: 425–440. Springer International Publishing.
Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M. and Bakker, P.J. (2008)
Application of process mining in healthcare–a case study in a Dutch hospital.
International joint conference on biomedical engineering systems and technologies:
425-438. Springer, Berlin, Heidelberg.
Rebuge, Á. and Ferreira, D.R. (2012). Business process analysis in healthcare environments:
A methodology based on process mining. Information systems, 37(2): 99-116.
Van der Aalst, W.M., Reijers, H.A., Weijters, A.J., van Dongen, B.F., De Medeiros, A.A.,
Song, M. and Verbeek, H.M.W. (2007). Business process mining: An industrial
application. Information Systems, 32(5): 713-732.