KAVAI: A Systematic Review of the Building Blocks for Knowledge-Assisted Visual Analytics in Industrial Manufacturing

Abstract

Industry 4.0 produces large volumes of sensor and machine data, offering new possibilities for manufacturing analytics but also creating challenges in combining domain knowledge with visual analysis. We present a systematic review of 13 peer-reviewed knowledge-assisted visual analytics (KAVA) systems published between 2014 and 2024, following PRISMA guidelines for the identification, screening, and inclusion processes. The survey is organized around six predefined building blocks, namely, user group, industrial domain, visualization, knowledge, data and machine learning, with a specific emphasis on the integration of knowledge and visualization in the reviewed studies. We find that ontologies, taxonomies, rule sets, and knowledge graphs provide explicit representations of expert understanding, sometimes enriched with annotations and threshold specifications. These structures are stored in RDF or graph databases, relational tables, or flat files, though interoperability is limited, and post-design contributions are not always persisted. Explicit knowledge is visualized through standard and specialized techniques, including thresholds in time-series plots, annotated dashboards, node–link diagrams, customized machine views from ontologies, and 3D digital twins with expert-defined rules. Line graphs, bar charts, and scatterplots are the most frequently used chart types, often augmented with thresholds and annotations derived from explicit knowledge. Recurring challenges include fragmented storage, heterogeneous data and knowledge types, limited automation, inconsistent validation of user input, and scarce long-term evaluations. Addressing these gaps will be essential for developing adaptable, reusable KAVA systems for industrial analytics.