Performance mining for batch processing using the performance spectrum

Eva L. Klijn, Dirk Fahland

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize process performance indicators over time. These aggregation-based techniques are not able to detect and quantify the performance of time-dependent performance patterns such as batches. In this paper, we propose a first technique for mining performance features from the recently introduced performance spectrum. We present an algorithm to detect batches from event logs even in case of batches overlapping with non-batched cases, and we propose several measures to quantify batching performance. Our analysis of public real-life event logs shows that we can detect batches reliably, batching performance differs significantly across processes, across activities within a process, and our technique even allows to detect effective changes to batching policies regarding consistency of processing.
Original languageEnglish
Title of host publication15th International Workshop on Business Process Intelligence
Number of pages12
Publication statusE-pub ahead of print - 2019
Event15th International Workshop on Business Process Intelligence (BPI’19) - Vienna, Austria
Duration: 1 Sep 20196 Sep 2019

Conference

Conference15th International Workshop on Business Process Intelligence (BPI’19)
Abbreviated titleBPI'19
CountryAustria
CityVienna
Period1/09/196/09/19

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Klijn, E. L., & Fahland, D. (2019). Performance mining for batch processing using the performance spectrum. In 15th International Workshop on Business Process Intelligence
Klijn, Eva L. ; Fahland, Dirk. / Performance mining for batch processing using the performance spectrum. 15th International Workshop on Business Process Intelligence. 2019.
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abstract = "Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize process performance indicators over time. These aggregation-based techniques are not able to detect and quantify the performance of time-dependent performance patterns such as batches. In this paper, we propose a first technique for mining performance features from the recently introduced performance spectrum. We present an algorithm to detect batches from event logs even in case of batches overlapping with non-batched cases, and we propose several measures to quantify batching performance. Our analysis of public real-life event logs shows that we can detect batches reliably, batching performance differs significantly across processes, across activities within a process, and our technique even allows to detect effective changes to batching policies regarding consistency of processing.",
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Klijn, EL & Fahland, D 2019, Performance mining for batch processing using the performance spectrum. in 15th International Workshop on Business Process Intelligence. 15th International Workshop on Business Process Intelligence (BPI’19), Vienna, Austria, 1/09/19.

Performance mining for batch processing using the performance spectrum. / Klijn, Eva L.; Fahland, Dirk.

15th International Workshop on Business Process Intelligence. 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Performance mining for batch processing using the performance spectrum

AU - Klijn, Eva L.

AU - Fahland, Dirk

PY - 2019

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N2 - Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize process performance indicators over time. These aggregation-based techniques are not able to detect and quantify the performance of time-dependent performance patterns such as batches. In this paper, we propose a first technique for mining performance features from the recently introduced performance spectrum. We present an algorithm to detect batches from event logs even in case of batches overlapping with non-batched cases, and we propose several measures to quantify batching performance. Our analysis of public real-life event logs shows that we can detect batches reliably, batching performance differs significantly across processes, across activities within a process, and our technique even allows to detect effective changes to batching policies regarding consistency of processing.

AB - Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize process performance indicators over time. These aggregation-based techniques are not able to detect and quantify the performance of time-dependent performance patterns such as batches. In this paper, we propose a first technique for mining performance features from the recently introduced performance spectrum. We present an algorithm to detect batches from event logs even in case of batches overlapping with non-batched cases, and we propose several measures to quantify batching performance. Our analysis of public real-life event logs shows that we can detect batches reliably, batching performance differs significantly across processes, across activities within a process, and our technique even allows to detect effective changes to batching policies regarding consistency of processing.

UR - https://github.com/multi-dimensional-process-mining/psm-batchmining

M3 - Conference contribution

BT - 15th International Workshop on Business Process Intelligence

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Klijn EL, Fahland D. Performance mining for batch processing using the performance spectrum. In 15th International Workshop on Business Process Intelligence. 2019