Clustering Traces Using Sequence Alignment

Evermann, Jöerg; Thaler, Tom; Fettke, Peter

doi:10.1007/978-3-319-42887-1_15

Cited by 28 publications

(20 citation statements)

References 23 publications

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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%

“…do not support event logs preprocessing tasks that help improving the quality of event logs. There are particular tools, applications, or frameworks developed for specific preprocessing tasks of event logs [26,36,37,48,52,53,72]. Most of these tools are limited to a single process modeling language and use some type of data deployment or transformation.…”

Section: C2 Toolsmentioning

confidence: 99%

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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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Section: Detection-visualization Techniquesmentioning

confidence: 99%

Section: C2 Toolsmentioning

confidence: 99%

Event Log Preprocessing for Process Mining: A Review

Marin-Castro

Tello-Leal

2021

Applied Sciences

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“…Trace clustering is different from other process mining algorithms in discovering process models that are usually spaghetti-like, especially from event logs that have highly flexible environments, which are difficult to analyze [21], [22]. Trace clustering can be used to make the obtained model simpler by identifying homogeneous sets [9].…”

Section: B Trace-based Clustering and Cluster Evolution Analysismentioning

confidence: 99%

“…Originally, trace clustering grouped cases as a full trace without considering partial traces (i.e., time-based trace or temporal case). The result of trace clustering produces homogeneous sets of traces from a full event log and constructs simpler process models [9]- [12]. Some works have used clustering to group students based on specific indicators (i.e., performance and activity) to obtain more specific and accurate models of students' behavior [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Profile-Based Cluster Evolution Analysis: Identification of Migration Patterns for Understanding Student Learning Behavior

Priyambada

Yahya

et al. 2021

IEEE Access

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Educational process mining is one of the research domains that utilizes students' learning behavior to match students' actual courses taken and the designed curriculum. While most works attempt to deal with the case perspective (i.e., traces of the cases), the temporal case perspective has not been discussed. The temporal case perspective aims to understand the temporal patterns of cases (e.g., students' learning behavior in a semester). This study proposes an extension of cluster evolution analysis, called profile-based cluster evolution analysis, for students' learning behavior based on profiles. The results show three salient features: (1) cluster generation; (2) within-cluster generation; and (3) time-based between-cluster generation. The cluster evolution phase extends the existing cluster evolution analysis with a dynamic profiler. The model was tested on actual educational data of the Information System Department in Indonesia. The results showed the learning behavior of students who graduated on time, the learning behavior of students who graduated late, and the learning behavior of students who dropped out. Students changed their learning behavior by observing the migration of students from cluster to cluster for each semester. Furthermore, there were distinct learning behavior migration patterns for each category of students based on their performance. The migration pattern can suggest to academic stakeholders students who are likely to drop out, graduate on time or graduate late. These results can be used as recommendations to academic stakeholders for curriculum assessment and development and dropout prevention.

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“…The results showed that the clustering approach allows auditors to distinguish different process variants within a timeframe. The approach used distance metrics between activities of workflow change and evolution-aware security audits (Accorsi and Stocker [19]). Evermann et al [20] proposed trace clustering method based on a local alignment of sequences, subsequent multidimensional scaling, and k-means clustering to discover simpler models.…”

Section: Trace Clusteringmentioning

confidence: 99%

Curriculum Assessment of Higher Educational Institution Using Trace-segmented Clustering

Priyambada

Yahya

2018

jti

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Curriculum mining is research area that assess students’ learning behavior and compare it with the curriculum guideline. Previous work developed sequence matching alignment approach to check the conformance between students’ learning behavior and curriculum guideline. Considering only the sequence matching alignment is insufficient to understand the patterns of group of students. Another work proposed an approach by aggregating the students’ profile to represent students’ learning behavior and investigate the impact of the learning behavior to their learning performance. However, the aggregate profile approach considers the entire period of study rather than segmented period. This study proposes a methodology to assess students’ learning path with segmented period i.e. the semester of the related curriculum. The segmented-period profile generated would be the input for sequence matching alignment approach to assess the conformity of students’ behavior with the prior curriculum guideline. Real curriculum data has been used to test the effectivity of the methodology. The results show that the students can be grouped into various cluster per semesters that have different characteristic with respect to their learning behavior and performance. The results can be analyzed further to improve the curriculum guideline.

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Clustering Traces Using Sequence Alignment

Cited by 28 publications

References 23 publications

Event Log Preprocessing for Process Mining: A Review

Event Log Preprocessing for Process Mining: A Review

Profile-Based Cluster Evolution Analysis: Identification of Migration Patterns for Understanding Student Learning Behavior

Curriculum Assessment of Higher Educational Institution Using Trace-segmented Clustering

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