Abstract
Original language | English |
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Title of host publication | 15th International Workshop on Business Process Intelligence |
Number of pages | 12 |
Publication status | E-pub ahead of print - 2019 |
Event | 15th International Workshop on Business Process Intelligence (BPI’19) - Vienna, Austria Duration: 1 Sep 2019 → 6 Sep 2019 |
Conference
Conference | 15th International Workshop on Business Process Intelligence (BPI’19) |
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Abbreviated title | BPI'19 |
Country | Austria |
City | Vienna |
Period | 1/09/19 → 6/09/19 |
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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 proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Performance mining for batch processing using the performance spectrum
AU - Klijn, Eva L.
AU - Fahland, Dirk
PY - 2019
Y1 - 2019
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
ER -