Generating time-based label refinements to discover more precise process models

Tax, Niek; Alasgarov, Emin; Sidorova, Natalia; Haakma, Reinder; Aalst, Wil M. P. van der

doi:10.3233/ais-190519

Cited by 11 publications

(4 citation statements)

References 47 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But they did not allow to discover meaningful activities for our data set. For unlabelled training sets, related approaches suggest to use a time-based label refinement [24] or locations [25] as characteristics in order to segment the event log and to abstract activities out of it. However, the methods already expects particular representations of traces.…”

Section: Related Workmentioning

confidence: 99%

Process Model Discovery from Sensor Event Data

Janssen

Mannhardt

Koschmider

et al. 2021

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Virtually all techniques, developed in the area of process mining, assume the input event data to be discrete, and, at a relatively high level (i.e., close to the business-level). However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, e.g., sensor data. Hence, in this paper, we present a novel technique that allows us to translate sensor data into higher-level, discrete event data, thus enabling existing process mining techniques to work on data tracked at a sensory level. Our technique discretises the observed sensor data into activities by applying unsupervised learning in the form of clustering. Furthermore, we refine the observed sequences by deducing imperative sub-models for the observed discretised data, i.e., allowing us to identify concurrency and interleaving within the data. We evaluated the approach by comparing the obtained model quality for several clustering techniques on a publicly available data-set in a smart home scenario. Our results show that applying our framework combined with a clustering technique yields results on data that otherwise would not be suitable for process discovery.

show abstract

Section: Related Workmentioning

confidence: 99%

Process Model Discovery from Sensor Event Data

Janssen

Mannhardt

Koschmider

et al. 2021

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

show abstract

“…Tax et al [31] proposed a framework for the automated generation of label refinements based on the time attribute of events, allowing to distinguish behaviorally different instances of the same event type based on their time attributes. The events generated by one sensor were clustered using a mixture model consisting of components of the von Mises distribution, which is the circular equivalent of the normal distribution.…”

Section: Transformation Techniquesmentioning

confidence: 99%

Event Log Preprocessing for Process Mining: A Review

Marin-Castro

Tello-Leal

2021

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

“…The improvement of the quality of activity labels has been studied in the process mining literature [17]- [20]. Some approaches are at the process model level [17], [18], [21], and some others are at the event log level [19], [20], which mainly focus on detecting and repairing activity labels with the same syntax but different semantics (i.e., homonymous labels [3]). Activity quality improvement has been conducted through the use of domain knowledge [22]- [25], which can be sourced from an existing domain ontology [18], or it can be provided by humans (either a few individuals [22], [23], or a crowd [24], [25]).…”

Section: Related Workmentioning

confidence: 99%

Process Activity Ontology Learning From Event Logs Through Gamification

et al. 2021

View full text Add to dashboard Cite

The quality of event log data is a constraining factor in achieving reliable insights in process mining. Particular quality problems are posed by activity labels which are meant to be representative of organisational activities, but may take different manifestations (e.g. as a result of manual entry synonyms may be introduced). Ideally, such problems are remedied by domain experts, but they are time-poor and data cleaning is a time-consuming and tedious task. Ontologies provide a means to formalise domain knowledge and their use can provide a scalable solution to fixing activity label similarity problems, as they can be extended and reused over time. Existing approaches to activity label quality improvement use manuallygenerated ontologies or ontologies that are too general (e.g. WordNet). Limited attention has been paid to facilitating the development of purposeful ontologies in the field of process mining. This paper is concerned with the creation of activity ontologies by domain experts. For the first time in the field of process mining, their participation is facilitated and motivated through the application of techniques from crowdsourcing and gamification. Evaluation of our approach to the construction of activity ontologies by 35 participants shows that they found the method engaging and that its application results in high-quality ontologies.

show abstract

Generating time-based label refinements to discover more precise process models

Cited by 11 publications

References 47 publications

Process Model Discovery from Sensor Event Data

Process Model Discovery from Sensor Event Data

Event Log Preprocessing for Process Mining: A Review

Process Activity Ontology Learning From Event Logs Through Gamification

Contact Info

Product

Resources

About