Translating Message Sequence Charts to other Process Languages Using Process Mining

Lassen, Kristian Bisgaard

doi:10.1007/978-3-540-89287-8_5

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Lorenz et al define partially ordered runs of Petri nets in order to analyze properties of Petri nets [16]. Lassen et al presented an approach to convert basic message sequence charts into p-traces and used these explicit casual dependencies to improve the process discovery result [17]. Fahland and Van der Aalst used partially ordered runs to simplify process models [11].…”

Section: Resultsmentioning

confidence: 99%

Conformance Checking Based on Partially Ordered Event Data

Fahland

Aalst

2015

Business Process Management Workshops

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Abstract. Conformance checking is becoming more important for the analysis of business processes. While the diagnosed results of conformance checking techniques are used in diverse context such as enabling auditing and performance analysis, the quality and reliability of the conformance checking techniques themselves have not been analyzed rigorously. As the existing conformance checking techniques heavily rely on the total ordering of events, their diagnostics are unreliable and often even misleading when the timestamps of events are coarse or incorrect. This paper presents an approach to incorporate flexibility, uncertainty, concurrency and explicit orderings between events in the input as well as in the output of conformance checking using partially ordered traces and partially ordered alignments, respectively. The paper also illustrates various ways to acquire partially ordered traces from existing logs. In addition, a quantitative-based quality metric is introduced to objectively compare the results of conformance checking. The approach is implemented in ProM plugins and has been evaluated using artificial logs.

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Section: Resultsmentioning

confidence: 99%

Conformance Checking Based on Partially Ordered Event Data

Fahland

Aalst

2015

Business Process Management Workshops

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“…The firing rule (described in the previous paragraph) generates sequences of transitions which can be converted into sequences of activities by applying the λ-function. However, partially ordered semantics can be expressed by several process modeling formalisms as well, including BPMN [8], Message Sequence Charts [9] and Petri nets [10]. In most process modeling formalisms, the execution of two activities a, b∈A may be independent, that is, the execution of a does not directly influence the execution of b and vice versa.…”

Section: Figmentioning

confidence: 99%

“…The authors in [9] use Message Sequence Charts (MSCs), which denote messages sent between processes, with the messages sent for one process being totally ordered. Each MSC is translated into a partially ordered trace.…”

Section: Certain Semanticsmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms.

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“…Both process discovery and process conformance checking techniques often assume a total ordering of events. Lassen et al discussed an approach to convert basic message sequence charts (MSCs) that already have explicit partial order structure into p-traces to synthesize a high-quality process model from MSCs [17]. Fahland and Van der Aalst used partially ordered runs of fitting traces to simplify process models [13].…”

Section: Related Workmentioning

confidence: 99%

Conformance checking in healthcare based on partially ordered event data

Mans

Fahland

et al. 2014

Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)

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Abstract-There is a continuous pressure to make healthcare processes more efficient and effective without sacrificing quality. Conformance checking can be used to improve processes by analyzing event data and directly relating observed behavior and modeled behavior. Conformance checking provides diagnostics that go far beyond measuring traditional key performance indicators. However, current conformance checking techniques focus on a rather simplistic setting where executions of process instances are sequential and homogeneous whereas healthcare processes are known to be dynamic, complex, and ad-hoc. In healthcare process instances of patients often follow a unique path through the process with one-of-a-kind deviations. Moreover, timestamps are often rather coarse (the date is known, but not the time) resulting in an unreliable ordering of events. As current techniques are unable to handle concurrent events, and the obtained sequential alignments are unable to provide structural information about deviations, the diagnostics provided are often insufficient and misleading. This paper presents a novel approach using partially ordered traces and partially ordered alignments which aims to incorporate unreliability and concurrency in the input while providing diagnostics about deviations that take causalities into account. The approach has been implemented in ProM and was evaluated using event data from a Dutch hospital.

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Translating Message Sequence Charts to other Process Languages Using Process Mining

Cited by 8 publications

References 26 publications

Conformance Checking Based on Partially Ordered Event Data

Conformance Checking Based on Partially Ordered Event Data

Partial-order-based process mining: a survey and outlook

Conformance checking in healthcare based on partially ordered event data

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