Unveiling Causality in Stateful Enterprise Knowledge Graphs: An Exploration of Process-Driven Object Relationships - Schobert, Marvin; Mala, Matúš; Herhoffer, Markus; Farag, Samuel; Franke, Jörg (Procedia CIRP, 2024)
The exponential growth of enterprise data generation demands innovative approaches for efficient information management across highly interconnected domains. Particularly, the process of systems engineering involves inhomogeneous data sets requiring a high degree of flexibility and traceability throughout its lifecycle. While structured data resides comfortably in dedicated systems, managing unstructured information within annotations, documents and emails often comes with challenging holistic comprehension of linking, context and revisioning. Graph technology holds promise in establishing adaptable connections among decentralized data sets. However, leveraging graph databases comes with challenges. Schema standardization, crucial for framing complex processes, is a difficult undertaking due to evolving data interrelationships and changing requirements. This paper proposes a two-fold solution. First, we introduce a conceptual schema that decouples stateful object descriptions from its headers and furthermore documents transactions. This allows flexible object associations, conditional state versioning and improved traceability while maintaining manageability. Second, we leverage the Business Process Model and Notation (BPMN) for process design, service orchestration and automated graph manipulations. Through application to the publicly available Northwind data set using neo4j and Flowable BPM, we illustrate our methodology's efficacy and the seamless integration of type-safer data science into dynamic knowledge graphs, contributing to the future of enterprise data management.
CRIS-Team Scopus Importer:2024-11-22
Keywords: BPMN; Design informatics; Knowledge Graphs; neo4j; Provenance