Scalable object-relational modeling for synthesizing multi-format visual analytics of STIX-based cyber threat intelligence data

The observed increase in cyber threat intelligence data, the diversity of sources, and relational complexity make it difficult for analysts to directly assess and interpret threat profiles from raw Structured Threat Information Expression (STIX) packages. This study utilises an object-relational model to transform all object and relationship types into a single unified representation and reconstruct the same graph model across three different visualisation software packages (plotly, matplotlib, pyvis). The proposed analytical framework generates extended threat models by combining multiple STIX datasets through entity extraction, creating observable entities from each dataset, and completing relationships among them; thus mapping relationship types to the operational meanings of attacks and enabling more comprehensive risk prioritisation. The case study is based on a public OASIS STIX example package. The enriched graph is dominated by relationship types such as uses, indicates, pattern-refers-to, and attributed-to, and the highest risk scores are assigned to the nodes labelled “Privilege Escalation” and “Ugly Gorilla”. Scalability tests performed on 1000-node synthetic directed graphs revealed a processing time of 8.08 seconds, memory consumption of 124.3 MB, and a frame rate of 215.05 fps during interactive preview. These findings demonstrate that the proposed framework offers a unified and viable foundation for visual analysis, risk prioritisation, and scalable analytical reporting of STIX-based cyber threat intelligence.