🤖 AI Summary
In manufacturing digital transformation, low-efficiency fusion of multi-source heterogeneous production event data and highly customized preprocessing hinder scalability and consistency. Method: This paper proposes a standardized reference model integrating the ISA-95 standard with an event knowledge graph, formally deriving reusable and automatable generic event data augmentation patterns. It pioneers the coupling of ISA-95’s hierarchical semantics with the structured representation of event knowledge graphs, employing empirically driven pattern engineering for modeling and validation. Contribution/Results: We establish an industrially applicable unified framework for event data storage and extraction, and deliver a validated suite of automation-ready data augmentation patterns. Evaluated across multiple real-world manufacturing scenarios, the approach significantly reduces preprocessing customization effort and deployment time, while enhancing scalability and analytical consistency of event data processing.
📝 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.