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.
|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||15th International Workshop on Business Process Intelligence (BPI’19)|
|Period||1/09/19 → 6/09/19|