A Reference Model and Patterns for Production Event Data Enrichment

TL;DR

This paper presents a standardized reference model and reusable patterns for automating production event data enrichment, improving efficiency in data processing for manufacturing insights.

Contribution

It introduces a novel reference model combining ISA-95 and Event Knowledge Graphs, along with empirically derived patterns for automating data enrichment tasks.

Findings

Patterns effectively automate common information extraction tasks.

Application to manufacturing datasets demonstrates pattern relevance.

The reference model standardizes production event data handling.

Abstract

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.