Automatic Repair of Same-Timestamp Errors in Business Process Event Logs

Abstract

This paper contributes an approach for automatically correcting "same-timestamp" errors in business process event logs. These errors consist in multiple events exhibiting the same timestamp within a given process instance. Such errors are common in practice and can be due to the logging granularity or the performance load of the logging system. Analyzing logs that have not been properly screened for such problems is likely to lead to wrong or misleading process insights. The proposed approach revolves around two techniques: one to reorder events with same-timestamp errors, the other to assign an estimated timestamp to each such event. The approach has been implemented in a software prototype and extensively evaluated in different settings, using both artificial and real-life logs. The experiments show that the approach significantly reduces the number of inaccurate timestamps, while the reordering of events scales well to large and complex datasets. The evaluation is complemented by a case study in the meat & livestock domain showing the usefulness of the approach in practice.

Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

“…Our work, therefore, constitutes a starting point to design a set of axioms for the definition of measures [27]. Finally, a natural extension to this work is to provide the user with an opportunity to repair the detected timestamp issues in a similar manner to those presented in [8,9]. Our vision is to provide an integrated approach to detecting, quantifying, repairing and tracking (timestamp) quality issues in event logs.…”

“…This aims, for instance, to identify date specifications in the format common in the United States ('MM-DD-YYYY'). Studies also discover timestamp issues through stacked or parallel events [8,19]. For instance, let the doctor submit a form at the end of each examination in which he declares having 'measured blood pressure', 'measured temperature', and 'intercepted airways'.…”

Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log

“…Quality of data can be improved by (i) improving the way in which data are captured while they are being generated and (ii) improving the data after they have been acquired [8]. The above studies [10,11] are (ii), while our study is (i). By using these two perspective methods together, it is expected that data quality can be further improved.…”

“…Various studies have been done on the quality of event logs. Many of these studies have proposed algorithms for repairing defects in the event log, such as the presence of missing values in activities and timestamps [3,10,11,16,[20][21][22][23][24][25][26][27][28][29][30][31]. By using these methods, we can obtain higher quality data that are close to the actual business process from the data with missing values.…”

“…Sim et al proposed a likelihood-based Multiple Imputation to complete the missing values for events in the event log [10]. Conforti et al proposed a method for repairing the timestamp with which an event was executed in the event log [11]. By using these methods, it is possible to complement the missing value in the event log with the predicted value.…”