Abstract Event log data frequently contain incomplete and incorrect entries due to errors or hardware malfunctions during the logging process. Poor event log quality hinders the application of business process analysis methods. Traditional approaches to handling business process event logs involve imputing the affected values i.e. replacing them with reliable estimations. To this end, machine learning approaches have been proposed, mainly deep neural network-based ones. Interestingly, data imputation for arbitrary time series encompasses a broader range of techniques, categorized into matrix-based, profile-based, and neural network-based methods. However, such techniques are not readily applicable to business process logs due to the fact that the latter consist of timestamped sequences of varying length and semantics, e.g., the same position in the trace may correspond to a different activity. Our contribution is firstly to show that dealing with the varying length problem using state-of-the-art zero padding based autoencoders is suboptimal and then to propose a pre-processing grouping framework, where imputation of missing values is based solely on the members of the same group. We introduce three variants of groupings, with one of them being counter-intuitive in the sense that it leverages logs of different sequential patterns, but is particularly effective. More importantly, the manner in which we approach the problem gives rise to a novel trade-off, namely accuracy vs. coverage, which has not been investigated before. The presented solutions are evaluated against a baseline imputation method and a state-of-the-art methodology, and the decrease in errors reached 3 to 10 times.
Varvoutas et al. (Fri,) studied this question.