Normalizing object-centric process logs by applying database principles. Issue 115 (May 2023)

• Record Type: Journal Article
• Authors: Kumar, Akhil; Soffer, Pnina; Tsoury, Arava
• Is Part Of: Information systems. Issue 115(2023)
• Publication Date: 2023-05
• Subjects: Object-centric logs -- Database normalization -- Process mining; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2023.102196
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Much work has been done in process mining in the last two decades, where the focus of most efforts has been on unearthing the process models from log traces where each trace could be related to a unique case identifier that pertains to a single instance, such as an online customer order, a production order, a patient visit, etc. The case identifiers in these cases are customer order number, production order number, patient id, respectively, and there is a one-to-one relationship between the case identifier and the log data. On the other hand, in so-called object-centric (OC) logs, multiple objects are associated in one log record giving rise to many-to-many relationships among these objects and leading to ambiguities and redundancies in the log data. Hence, these logs become very difficult to analyze in their raw form as single linear files and it is important to convert them into database models. In this paper, we show how OC logs can be structured into a STAR and a fully normalized database schemas. The two schemas are compared and the benefits of our approach for log processing and ensuring log integrity are discussed. Highlights: Object-centric logs used in process mining suffer from inconsistencies and redundancies. Conducted an in-depth analysis of object-centric logs and showed how to restructure them. Proposed two new schemas — A Star schema and a fully normalized schema compared them. Tested our approach on a database system using data from an object-centric log. Showed how log integrity can be checked after normalizing it.