bupaR: Enabling reproducible business process analysis

Janssenswillen, Gert; Depaire, Benoît; Swennen, Marijke; Jans, Mieke; Vanhoof, Koen

doi:10.1016/j.knosys.2018.10.018

Cited by 63 publications

(34 citation statements)

References 9 publications

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“…The practical ETT implementation was done in the R programming language, due to the availability of the supporting packages such as bupaR [10]. We used R environments for the hash table implementation, based on the benchmark testing in [35].…”

Section: Resultsmentioning

confidence: 99%

“…We selected the 3-way matching, invoice before goods receipt process flow comprising only EC documents. After importing the XES dataset file into the R environment using xesreadR package [10], we selected activities belonging to the selected process flow. From the ETT definition, the most critical are activities performed by the Supplier Relationship Management (SRM) system.…”

Section: E Bpi Challenge 2019mentioning

confidence: 99%

“…The first process discovery test-case is done using the business process analysis R package named bupaR [10]. In the bupaR package, there are multiple real-life event logs for process discovery purposes, where we selected the event log comprising hospital process activities for treating septic patients.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Latent Process Discovery Using Evolving Tokenized Transducer

2019

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Today organizations capture and store an abundant amount of data from their interaction with clients, internal information systems, technical systems and sensors. Data captured this way comprises many useful insights that can be discovered by various analytical procedures and methods. Discovering regular and irregular data sequences in the captured data can reveal processes performed by the organization, which can be then assessed, measured and optimized, to achieve overall better performance, lower costs, resolve congestions, find potentially fraudulent activities and similar. Besides process discovery, capturing data sequences can give additional behavioral and tendency insights for various observations in the organization, such as sales dynamic, customer behaviour and similar. The issue is that most of the captured data intertwine multiple processes, customers, cases, products in a single data log or data stream. In this article, we propose an evolving tokenized transducer (ETT), capable of learning data sequences from a multi-contextual data log or stream. The proposed ETT is a semi-supervised relational learning method that can be used as a classifier on an unknown data log or stream, revealing previously learned data sequences. The proposed ETT was tested on multiple synthetic and real-life cases and datasets, such as dataset of retail sales sequences, hospital process log involving septic patient treatment and BPI challenge 2019 dataset. Test results are successful, revealing ETT as a prominent process discovery method.

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Section: Resultsmentioning

confidence: 99%

Section: E Bpi Challenge 2019mentioning

confidence: 99%

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Latent Process Discovery Using Evolving Tokenized Transducer

2019

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“…An event log was generated by converting the data according to the proposed analysis model, in order to answer the research question. After that, model discovery and model analysis were performed using bupaR, an integrated collection of packages that creates a framework for the reproducible analysis of processes in R [41]. R is a free software environment for statistical computing and graphics [42].…”

Section: Methodsmentioning

confidence: 99%

Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout

et al. 2021

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Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational trajectories of undergraduate students in high-failure rate courses help to describe the process that leads to late dropout. Educational trajectories (n = 10,969) of high-failure rate courses are created using Process Mining techniques, and the results are discussed based on established theoretical frameworks. Late dropout was more frequent among students who took a stopout while having high-failure rate courses they must retake. Furthermore, students who ended in late dropout with high-failure rate courses they must retake had educational trajectories that were on average shorter and less satisfactory. On the other hand, the educational trajectories of students who ended in late dropout without high-failure rate courses they must retake were more similar to those of students who graduated late. Moreover, some differences found among ISCED fields are also described. The proposed approach can be replicated in any other university to understand the educational trajectories of late dropout students from a longitudinal perspective, generating new knowledge about the dynamic behavior of the students. This knowledge can trigger improvements to the curriculum and in the follow-up mechanisms used to increase student retention.

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“…The trace-level behaviour was described by the durations and variants of the traces in the sub-logs using bupaR [ 20 ]. The duration of referral to diagnosis was analysed further in relation to the new 28-day waiting time target as set by the Cancer Task Force.…”

Section: Methodsmentioning

confidence: 99%

Using a Multi-Level Process Comparison for Process Change Analysis in Cancer Pathways

Kurniati

McInerney

Zucker

et al. 2020

IJERPH

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The area of process change over time is a particular concern in healthcare, where patterns of care emerge and evolve in response to individual patient needs. We propose a structured approach to analyse process change over time that is suitable for the complex domain of healthcare. Our approach applies a qualitative process comparison at three levels of abstraction: a holistic perspective (process model), a middle-level perspective (trace), and a fine-grained detail (activity). Our aim was to detect change points, localise and characterise the change, and unravel/understand the process evolution. We illustrate the approach using a case study of cancer pathways in Leeds where we found evidence of change points identified at multiple levels. In this paper, we extend our study by analysing the miners used in process discovery and providing a deeper analysis of the activity of investigation in trace and activity levels. In the experiment, we show that this qualitative approach provides a useful understanding of process change over time. Examining change at three levels provides confirmatory evidence of process change where perspectives agree, while contradictory evidence can lead to focused discussions with domain experts. This approach should be of interest to others dealing with processes that undergo complex change over time.

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bupaR: Enabling reproducible business process analysis

Cited by 63 publications

References 9 publications

Latent Process Discovery Using Evolving Tokenized Transducer

Latent Process Discovery Using Evolving Tokenized Transducer

Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout

Using a Multi-Level Process Comparison for Process Change Analysis in Cancer Pathways

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