Declarative Process Mining: Reducing Discovered Models
Added on 2021-05-30
8 Pages2413 Words184 Views
Theoretical Computer ScienceCalculus and Analysis
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Business Processes 1DECLARATIVE PROCESS MINING: REDUCING DISCOVERED MODELS COMPLEXITYBY PRE-PROCESSING EVENT LOGSby (Name)The Name of the Class (Course)Professor (Tutor)The Name of the School (University)The City and State where it is locatedThe DateDeclarative Process Mining: Reducing Discovered Models Complexity by Pre-Processing EventLogs
Business Processes 2Introduction It is worth pointing out that procedural process mining techniques mainly work withprocedural modeling procedural languages where all possible orderings of events must bespecified. But, using such approaches when used to explain procedures for changingenvironments can extremely difficult. Therefore, instead, firms may use declarative processmodels to ensure flexibility inn process description. This paper seeks to provide an analysis tothe article “Declarative Process Mining: Reducing Discovered Models Complexity by Pre-Processing Event Logs.” This article was authored by Pedro H. Piccoli Richetti, FernandaAraujo Baião, and Flávia Maria Santoro, from the department of applied informatics at theFederal University of the State of Rio de Janeiro. According to the authors, the discovery of thedeclarative process models by mining event logs aim at presenting unstructured but flexibleprocesses, thereby making them noticeable to businesses and enhance their manageability(Richetti et al., 2014). Therefore, this paper will analyze the article by examining various aspectsof the report. Firstly, the content and intention of the paper shall be discussed to establish thereason behind which the article was authored in the first place. Afterwards, the research methodused in the paper as regards to its survey, case study, observation and analysis shall be examinedand discussed. Afterwards, the paper shall record and review the findings of Richetti et al. asregards to the subject matter. Additionally, we shall also comment on the problems and issueshighlighted by the authors of the article while comparing them with those highlighted by othertwo articles discussing the topic. Therefore, this paper seeks to provide an analysis and review ofthe article written by Richetti et al (2014).Analysis of the ArticleIntention and content of the paperAccording to Richetti et al. (2014), process mining techniques permit for the extraction ofknowledge from procedures stored by information systems. These techniques also provide animperative connection between the data mining and business process management. Particularly,the use of these systems and interest in the subject has grown over the years due to theadvancements in process management as well as computer technology which allow more detailsto be registered. There has also been a growing need for improvements to be made to help
Business Processes 3support business processes in the ever changing and competitive business environment. The newprocess is promising but has some major disadvantages. Mainly, these disadvantages arise fromthe fact that the declarative viewpoint may still give rise to approaches that are difficult tocomprehend owing to the big sizes and high number of activities. Therefore, the reader notes that the intentions of the authors of the paper was to present a modelthat may help reduce the complex nature of the declarative model by grouping activitiesaccording to inclusion and hierarchy semantic relations (Richetti, et al., 2014). Fundamentally,the article identifies an approach that can help organizations limit the complexities of thedeclarative model by arranging activities according to hierarchy semantic and inclusion relations.The authors show that a declarative approach gives a better sense of balance between supportand flexibility. Nonetheless, they may produce models with many constraints that may beincomprehensible for humans. Therefore, one also notes that Richetti et al. (2014) intended todeal with the challenge of sophistication of the models arising from the automatic processmining. Thus, they utilize an approach that reduces the complexity of the approach by creatingprocess hierarchies automatically in pre-processing time. All in all, the main purpose of theresearch was for them to determine whether a declarative process model is less complex than thetraditional approaches. Research Method MethodIn their study, Richetti et al (2014) apply a natural language processing to classify the commonobjectives between activity labels, and then arrange them into hierarchies. They also utilizedWordnet as the tool for searching for the holonyms and hypernyms semantic correlationsbetween words. Mainly, this was done to look for universal objectives that can be utilized ingathering activities in a sub-process. Activities were grouped into algorithms. Algorithm 1consisted of actions that have objects and actions related to common senses. On the other hand,Algorithm 2 proposes a strategy for grouping based on a graph presentation. A prototype forexecuting both Algorithms was implemented using Java language (Richetti et al., 2014).Additionally, the authors used Aux; Sense iliary Python NLTK3.0 scripts were utilized for thepart-of-speech tagging step (Richetti et al., 2014). PERL WordNet:SenseRelate::WordToSet5
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