Compound Trace Clustering to Generate Accurate and Simple Sub-Process Models

Sun, Yaguang; Bauer, Bernhard; Weidlich, Matthias

doi:10.1007/978-3-319-69035-3_12

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Jablonski et al [31] and Song et al [46], [47] used multiple features to cluster event logs. They presented the concept of trace profiles as features of an event log, such as the frequency of a sequential relation A > B in an event log used as a feature to cluster event logs into homogenous sets.…”

Section: ) Clustering-based Approachesmentioning

confidence: 99%

Complex Process Modeling in Process Mining: A Systematic Review

et al. 2022

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Process mining techniques are used to extract knowledge about the efficiency and compliance of an organization's business processes through process models. Real-life processes are unstructured, and applying process mining to discover such processes often results in complex process models that do not provide actionable insights. Several solutions have been presented to overcome this problem. However, the process mining domain lacks an explicit definition of complexity and its measurement. This vagueness results in ad-hoc solutions that vary according to the approach, modelling construct, and process properties. Additionally, the strength and limitations of the proposed solutions have not been adequately highlighted. Therefore, we conducted a systematic literature review on complexity in process mining over six popular scholarly indexing databases. Based on the review results, an explicit definition of complexity, the main contributing factors and their impact on process mining results were identified. We discovered various process complexity matrices and their application context. The analysis of studies led to the development of a taxonomy consisting of four different approaches for addressing the complexity problem, along with their strengths and limitations. Finally, the open research challenges and potential for future research are discussed.

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Section: ) Clustering-based Approachesmentioning

confidence: 99%

Complex Process Modeling in Process Mining: A Systematic Review

et al. 2022

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“…Several techniques have been proposed in the last decade for trace clustering. They can be divided into three approaches: vector space approaches [34][35][36][37], context aware approaches [38][39][40][41][42], and model-based approaches [43][44][45][46][47][48]. Most of the clustering algorithms aforementioned consider only the event log as input, and use different internal representations for producing the clusters.…”

Section: Detection-visualization Techniquesmentioning

confidence: 99%

Event Log Preprocessing for Process Mining: A Review

Marin-Castro

Tello-Leal

2021

Applied Sciences

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Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

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“…If a set of numerical representations cannot well represent the running relationship of events in the trace, the similarity between traces in the clustering results may be very low, which ultimately affects the quality of the process model. Researchers explored more methods to improve the result of trace clustering and discover accurate and simple business process models from event logs [9,17,10,30,32].…”

Section: Related Workmentioning

confidence: 99%

A repairing missing activities approach with succession relation for event logs

Liu

Zhang

et al. 2020

Knowl Inf Syst

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In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company's information system to be unable to keep a complete event log. Based on a incomplete event log, the process model obtained by using existing process mining technologies is deviated from actual business process to a certain degree. In this paper, we propose a method for repairing missing activities based on succession relation of activities from event logs. We use an activity relation matrix to represent the event log and cluster it. The number of traces in the cluster is used as a measure of similarity calculation between incomplete traces and cluster results. Parallel activities in selecting pre-occurrence and post-occurrence activities of missing activities from incomplete traces are considered. Experimental results on real-life event logs show that our approach performs better than previous method in repairing missing activities.

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Compound Trace Clustering to Generate Accurate and Simple Sub-Process Models

Cited by 16 publications

References 16 publications

Complex Process Modeling in Process Mining: A Systematic Review

Complex Process Modeling in Process Mining: A Systematic Review

Event Log Preprocessing for Process Mining: A Review

A repairing missing activities approach with succession relation for event logs

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