Extracting Multiple Viewpoint Models from Relational Databases

Abstract

Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in reallife processes often different case notions are intertwined. For example, events of the same order handling process may refer to customers, orders, order lines, deliveries, and payments. Therefore, we propose to use Multiple Viewpoint (MVP) models that relate events through objects and that relate activities through classes. The required event data are much closer to existing relational databases. MVP models provide a holistic view on the process, but also allow for the extraction of classical event logs using different viewpoints. This way existing process mining techniques can be used for each viewpoint without the need for new data extractions and transformations. We provide a toolchain allowing for the discovery of MVP models (annotated with performance and frequency information) from relational databases. Moreover, we demonstrate that classical process mining techniques can be applied to any selected viewpoint.

Multi-Dimensional Event Data in Graph Databases

“…The directly-follows graphs of different entities and relations share nodes. We thereby obtain a method for discovering process models which describe the behavior of a network of related entities and their interactions as a single multi-viewpoint model [21].…”

“…1(2), which violates R0 and prevents R6 and R8. They are usually transformed to other formats for analysis [19,21].…”

“…Berti et al [21] convert object-centric logs (Sect. 2.3.2) into two separate graphs: One describes correlation of events to entities, and one describes the directly-follows relation between any two events per entity similar to [22,23].…”

“…However, in most event data in practice [12,28], such as Fig. 1, relations are not reified yet, limiting the applicability of [21] as it does not support R13. Further, the model does not support querying the event data prior to aggregation (violates R6, R7, R8, R10) because correlation and directly-follows relations are stored in separate graphs.…”

“…Graph-based data models for event data [20][21][22][23] have been proposed as alternative to overcome these limitations. Graphs can describe relations between various entities and sequential information as paths in one data structure.…”