Mining Project-Oriented Business Processes - Article Review

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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