An Approach to Evaluate the Local Completeness of an Event Log

Abstract

Process mining links traditional model-driven Business Process Management and data mining by means of deriving knowledge from event logs to improve operational business processes. As an impact factor of the quality of process mining results, the degree of completeness of the given event log should be necessarily measured. In this paper an approach is proposed in the context of mining control-flow dependencies to evaluate the local completeness of an event log without knowing any information about the original process model. Experiment results show that the proposed approach works robustly and gives better estimation than approaches available.

A Survey on Concept Drift in Process Mining

“…The approach proposed in [40] applies the local completeness (LC) property of event logs defined in [81] for drift detection. The idea is that it is possible to assert (with a defined confidence level K) that a log satisfies LC in a limited length.…”

A novel completeness definition of event logs and corresponding generation algorithm

“…Hee et al () overcome the problem of completeness checking by taking a probabilistic point of view, where a probabilistic lower bound for log completeness for three subclasses of Petri‐nets, namely, WF‐nets, T‐WF‐nets and S‐WF‐nets is provided. Yang et al () proposed an approach to evaluate the local completeness of an event log without knowing any information about the original process model and later in Yang, Wen, Wang, and Wong (), the approach was improved for estimating the local completeness of a log that contains a small number of traces precisely. However, in this paper, we do not investigate the completeness checking method for the newly proposed #TAR complete logs.…”

“…There are mainly two kinds of completeness: global and local (Hee, Liu, & Sidorova, ). Global complete event logs contain all possible execution traces of process models while local complete ones only contain all direct succession (Yang, Wen, & Wang, ) relations or transition adjacent relations (TARs; Zha et al, ) between tasks. However, the globally complete event logs have larger capacities than the locally complete ones, and it costs much more generating time.…”