Visualizing Trace Variants from Partially Ordered Event Data

Schuster, Daniel; Schade, Lukas; Zelst, Sebastiaan J. van; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-98581-3_3

Cited by 5 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 from the loaded event log that satisfies the displayed query. We refer to [13] for an introduction to the trace variant visualization. As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Further, we present its implementation in the process mining software tool Cortado [14], which supports partially ordered event data. Query results are visualized by Cortado using a novel trace variant visualization [13]. Finally, we evaluate the performance of the query evaluation on real-life, publicly available event logs.…”

Section: Introductionmentioning

confidence: 99%

“…(a) Based on the variant definition presented in[13] (b) https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f(d) https://doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b(d) https://doi.org/10.4121/uuid:52fb97d4-4588-43c9-9d04-3604d4613b51 (e) https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5…”

mentioning

confidence: 99%

See 2 more Smart Citations

Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data

Schuster

Martini

Zelst

et al. 2022

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Event logs, as viewed in process mining, contain event data describing the execution of operational processes. Most process mining techniques take an event log as input and generate insights about the underlying process by analyzing the data provided. Consequently, handling large volumes of event data is essential to apply process mining successfully. Traditionally, individual process executions are considered sequentially ordered process activities. However, process executions are increasingly viewed as partially ordered activities to more accurately reflect process behavior observed in reality, such as simultaneous execution of activities. Process executions comprising partially ordered activities may contain more complex activity patterns than sequence-based process executions. This paper presents a novel query language to call up process executions from event logs containing partially ordered activities. The query language allows users to specify complex ordering relations over activities, i.e., control flow constraints. Evaluating a query for a given log returns process executions satisfying the specified constraints. We demonstrate the implementation of the query language in a process mining tool and evaluate its performance on real-life event logs.

show abstract

“…4 from the loaded event log that satisfies the displayed query. We refer to [13] for an introduction to the trace variant visualization. As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data

Schuster

Martini

Zelst

et al. 2022

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since we focus in this use case study on partially ordered event data, we use concurrency-aware variants, which were recently introduced [46]. In the remainder of this paper, we refer to concurrency-aware variants as variants.…”

Section: Trace Variantsmentioning

confidence: 99%

Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application

Schuster,

Benevento,

Aloini

et al. 2024

J Healthc Inform Res

Self Cite

View full text Add to dashboard Cite

Most process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result, process mining techniques function like black boxes with limited interaction options for analysts, such as simple sliders for filtering infrequent behavior. Recent research tries to break these black boxes by allowing process analysts to provide domain knowledge and guidance to process mining techniques, i.e., hybrid intelligence. Especially, in process discovery—a critical type of process mining—interactive approaches emerged. However, little research has investigated the practical application of such interactive approaches. This paper presents a case study focusing on using incremental and interactive process discovery techniques in the healthcare domain. Though healthcare presents unique challenges, such as high process execution variability and poor data quality, our case study demonstrates that an interactive process mining approach can effectively address these challenges. Graphical Abstract

show abstract

“…The technique also supports mixed granularity in the timestamp recording of events. Similarly, in [97], the authors propose a generalization of the "Variant Explorer. "…”

Section: Visualizationmentioning

confidence: 99%

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

Leemans

Zelst

2022

Knowl Inf Syst

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Visualizing Trace Variants from Partially Ordered Event Data

Cited by 5 publications

References 9 publications

Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data

Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data

Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application

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

Contact Info

Product

Resources

About