Detecting Changes in Process Behavior Using Comparative Case Clustering

Hompes, Bart F. A.; Buijs, Jcam Joos; Aalst, Wil M. P. van der; Dixit, Prabhakar M.; Buurman, Johannes

doi:10.1007/978-3-319-53435-0_3

Cited by 29 publications

(39 citation statements)

References 17 publications

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“…The methodology followed by the other ten papers [3], [11], [20], [29], [34], [37], [40], [44], [61], [62] was using available plug-ins and/ or functionalities in existing tools to solve the problem in their case study.…”

Section: E Methodologiesmentioning

confidence: 99%

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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Section: E Methodologiesmentioning

confidence: 99%

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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“…The quality of a discovered model may change over time, and, by consequence, the validity of the conformance tests formerly executed is jeopardized. Techniques for detecting quality drifts in PM have been proposed [5], [21], [22], [23], [24], [25] [20]. Also, there is no consensus on the criteria used to detect concept drift.…”

Section: Another Crucial Goal Is Concept Drift Detection (Cdd)mentioning

confidence: 99%

Evaluation Goals for Online Process Mining: A Concept Drift Perspective

Ceravolo

Tavares²,

Barbon

et al. 2022

IEEE Trans. Serv. Comput.

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Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution 5 of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and 6 detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent 7 years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying 8 a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and 9 techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection 10 is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions. Q1 11 Index Terms-Online process mining, event stream, requirements and goals, concept drift Ç 12 1 INTRODUCTION 13 P ROCESS Mining (PM) is a set of data science techniques 14 focused on the analysis of event logs [1]. Events are 15 recorded when executing a Business Process and collected 16 into cases, i.e., end to end sequences of events relevant to the 17 same process instance. Traditional PM algorithms were 18 designed to work offline, analyzing historical batches of logs 19 gathering the complete course of cases, if necessary with 20 multiple passes of analysis. This is, however, insufficient, 21 from a business standpoint, when the real-time assessment 22 of processes is crucial to timely manage resources and 23 quickly react to dysfunctional behaviors [2]. Today's fast-24 changing market requires systematic adjustments of pro-25 cesses in response to changes in the organization's operat-26 ing system or to trends emerging from the environment [3]. 27 Recently, the notion of online PM has emerged in reference 28 to analytics capable of handling real-time event streams [4], 29 [5]. An event stream differs from an event log because it is an 30 unbounded sequence of events ingested one-by-one and 31 allowing for limited actions in terms of iteration and memory 32 or time consumption [6].

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“…Consider the log {⟨C, D, E, F, G, A, B, H, L⟩, ⟨C, D , E, F, G, H, L, A, B⟩, ⟨B, A, C, D, E, F, G, H, L⟩, ⟨V, W, T, S, H, L, A, B⟩, ⟨V, W, T, S, A, B, H, L⟩}, which is a more elaborated example of the one shown in the Introduction. In this log, the first three traces contain the same event types and are part of the same clusters according to [32], [28,29] and [16]. If we used a global oracle like the α, this would regard A and B as concurrent, since both interleavings are present in the first three traces.…”

Section: Related Workmentioning

confidence: 99%

Local Concurrency Detection in Business Process Event Logs

Armas-Cervantes

Dumas

Rosa

et al. 2019

ACM Trans. Internet Technol.

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Process mining techniques aim at analysing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to overgeneralization. This paper proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques.

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Detecting Changes in Process Behavior Using Comparative Case Clustering

Cited by 29 publications

References 17 publications

Process mining in oncology: A literature review

Process mining in oncology: A literature review

Evaluation Goals for Online Process Mining: A Concept Drift Perspective

Local Concurrency Detection in Business Process Event Logs

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