A Reference Model and Patterns for Production Event Data Enrichment
For practitioners in manufacturing and process mining, this work provides a structured approach to event data enrichment, though it is incremental as it combines existing standards and formalisms.
The paper introduces a reference model and patterns to standardize and automate the enrichment of production event data, addressing the ad-hoc and time-consuming nature of data pre-processing. The reference model combines ISA-95 with Event Knowledge Graph formalism, and patterns are derived from manufacturing datasets and evaluated through use cases.
With the advent of digital transformation, organisations are increasingly generating large volumes of data through the execution of various processes across disparate systems. By integrating data from these heterogeneous sources, it becomes possible to derive new insights essential for tasks such as monitoring and analysing process performance. Typically, this information is extracted during a data pre-processing or engineering phase. However, this step is often performed in an ad-hoc manner and is time-consuming and labour-intensive. To streamline this process, we introduce a reference model and a collection of patterns designed to enrich production event data. The reference model provides a standard way for storing and extracting production event data. The patterns describe common information extraction tasks and how such tasks can be automated effectively. The reference model is developed by combining the ISA-95 industry standard with the Event Knowledge Graph formalism. The patterns are developed based on empirical observations from event data sets originating in manufacturing processes and are formalised using the reference model. We evaluate the relevance and applicability of these patterns by demonstrating their application to use cases.