Detection and Interactive Repair of Event Ordering Imperfection in Process Logs

Dixit, Prabhakar M.; Suriadi, Suriadi; Andrews, Robert; Wynn, Moe Thandar; Hofstede, Arthur H. M. ter; Buijs, Jorn op den; Aalst, Wil M. P. van der

doi:10.1007/978-3-319-91563-0_17

Cited by 31 publications

(25 citation statements)

References 24 publications

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“…For instance, an unreliable sensor may record timestamps in terms of milliseconds, even though the true moment of occurrence can only be guaranteed in terms of seconds. These cases could be detected using approaches such as proposed by Dixit et al [33]. Following such a detection, a pre-processing step could be employed that abstracts the unreliable timestamps to a granularity at which the uncertainty no longer exists, before employing our proposed approach.…”

Section: Discussionmentioning

confidence: 99%

Partial order resolution of event logs for process conformance checking

Leopold

Weidlich

2020

Decision Support Systems

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While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.

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Section: Discussionmentioning

confidence: 99%

Partial order resolution of event logs for process conformance checking

Leopold

Weidlich

2020

Decision Support Systems

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“…Thirdly, heuristics have been developed which tackle specific data quality issues, e.g. adding missing events [10], imputing missing case identifiers [3], and handling event ordering issues [11].…”

Section: Simulation For Capacity Management Decisions In Healthcare mentioning

confidence: 99%

The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study

Hulzen

Martin

Depaire

2021

Lecture Notes in Business Information Processing

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In healthcare, more and more process execution information is stored in Hospital Information Systems. This data, in conjunction with data-driven process simulation, can be used, e.g. to support hospital management with Capacity Management decisions. However, real-life event logs in healthcare often suffer from data quality issues, affecting the reliability of simulation results. In this work, we illustrate the effects of disregarding data quality issues on simulation outcomes and the importance of domain knowledge using a case study at the radiology department of a hospital. Current literature on data-driven process simulation acknowledges the need for domain expertise but does not provide a framework for conceptualising the involvement of domain experts. Therefore, we propose a novel conceptual framework which interactively involves experts during data-driven simulation model development.

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“…We intend to bridge this gap in research specifically for timestamp-related data quality issues since timestamps are the principal means for ordering events and the foundation for many use cases [9,12,13]. Precise timestamps are essential to reproduce the correct ordering of activities and, thus, to obtain accurate process models (discovery), to measure the alignment between the process model and the actual process flow (conformance), and to determine effectiveness and efficiency in the execution of activities (performance) [9,12]. In contrast, inaccurate and coarse timestamps often lead to convoluted process models that may result in erroneous analyses [9].…”

Section: Introductionmentioning

confidence: 99%

“…Precise timestamps are essential to reproduce the correct ordering of activities and, thus, to obtain accurate process models (discovery), to measure the alignment between the process model and the actual process flow (conformance), and to determine effectiveness and efficiency in the execution of activities (performance) [9,12]. In contrast, inaccurate and coarse timestamps often lead to convoluted process models that may result in erroneous analyses [9]. This paper, therefore, focuses on the following research question: How can we detect and quantify timestamp-related data quality issues in event logs?…”

Section: Introductionmentioning

confidence: 99%

“…Beyond the mentioned timestamp-related issues, we identified other factors that may impact Process Mining analysis. Mixed granularity of traces may cause incorrect event ordering [9]. For instance, in a hospital log, 'admission' may be recorded at second-level granularity, e.g., '03 Feb 2019 10:23:12', while within the same trace 'examination by the doctor' may be recorded at hour-level, e.g., '03 Feb 2019 10:00:00'.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

Fischer

Goel

Andrews

et al. 2020

Lecture Notes in Computer Science

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Timestamp information recorded in event logs plays a crucial role in uncovering meaningful insights into business process performance and behaviour via Process Mining techniques. Inaccurate or incomplete timestamps may cause activities in a business process to be ordered incorrectly, leading to unrepresentative process models and incorrect process performance analysis results. Thus, the quality of timestamps in an event log should be evaluated thoroughly before the log is used as input for any Process Mining activity. To the best of our knowledge, research on the (automated) quality assessment of event logs remains scarce. Our work presents an automated approach for detecting and quantifying timestamp-related issues (timestamp imperfections) in an event log. We define 15 metrics related to timestamp quality across two axes: four levels of abstraction (event, activity, trace, log) and four quality dimensions (accuracy, completeness, consistency, uniqueness). We adopted the design science research paradigm and drew from knowledge related to data quality as well as event log quality. The approach has been implemented as a prototype within the open-source Process Mining framework ProM and evaluated using three real-life event logs and involving experts from practice. This approach paves the way for a systematic and interactive enhancement of timestamp imperfections during the data preprocessing phase of Process Mining projects.

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Detection and Interactive Repair of Event Ordering Imperfection in Process Logs

Cited by 31 publications

References 24 publications

Partial order resolution of event logs for process conformance checking

Partial order resolution of event logs for process conformance checking

The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study

Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

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