Towards Event Log Querying for Data Quality: Let’s Start with Detecting Log Imperfections

Abstract

Process mining is, by now, a well-established discipline focussing on process-oriented data analysis. As with other forms of data analysis, the quality and reliability of insights derived through analysis is directly related to the quality of the input (garbage in - garbage out). In the case of process mining, the input is an event log comprised of event data captured (in information systems) during the execution of the process. It is crucial then that the event log be treated as a first-class citizen. While data quality is an easily understood concept little effort has been directed towards systematically detecting data quality issues in event logs. Analysts still spend a large proportion of any project in ‘data cleaning’, often involving manual and ad hoc tasks, and requiring more than one tool. While there are existing tools and languages that query event logs, the problem of different approaches for different log imperfections remains. In this paper we take the first steps to developing QUELI (Querying Event Log for Imperfections) a log query language that provides direct support for detecting log imperfections. We develop an approach that identifies capabilities required of QUELI and illustrate the approach by applying it to 5 of the 11 event log imperfection patterns described in [29]. We view this as a first step towards operationalising systematic, automated support for log cleaning.