Business process variant analysis based on mutual fingerprints of event logs

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.This research is partly funded by the Australian Research Council (DP180102839) and Spanish funds MINECO and FEDER (TIN2017-86727-C2-1-R).Peer ReviewedPostprint (author's final draft

Multi-Perspective Comparison of Business Process Variants Based on Event Logs (Extended Paper)

The comparison of business process variants based on event logs is a recurrent operation in the field of process mining. Existing approaches are restricted to intra-case relations, and more specifically, directly-follows relations such as "a task directly follows another one" or a "resource directly hands-off to another resource" within the same case. This paper presents a more general approach for log-based process variant comparison based on so-called perspective graphs. A perspective graph is a graph-based abstraction of an event log where a node represents any entity in an event log (task, resource, location, etc.) and an arc represents an arbitrary relation between these entities (e.g. directly-follows, co-occurs, hands-off to, works-together with, etc.) within or across cases. Statistically significant differences between two perspective graphs are captured in a so-called differential perspective graph, which allows us to compare two event logs from any given perspective. The proposed approach has been implemented as a proof of concept prototype in ProM. We illustrate the possibilities of the approach on real-life event logs and compare it to existing approaches for log-based process variant comparison