From Data to Insight: Entity Relationship Mapping for Space Domain Awareness Using the SPINIFEX Tool

As the space domain becomes increasingly congested, contested, and competitive, maintaining effective Space Domain Awareness (SDA) is evolving beyond the traditional task of cataloguing and tracking resident space objects (RSOs). Modern SDA requires higher-order understanding of how space systems are organized, interconnected, governed, and operationally employed. Spacecraft, ground stations, communication links, mission architectures, and supporting infrastructures do not exist as isolated assets; instead, they form complex, interdependent networks. Insight into these relationships enables deeper understanding of system behavior, operational intent, latent capability, and vulnerability, thereby supporting more informed decision-making across both strategic and operational contexts.

Within this evolving environment, the Australian Defence Science and Technology Group (DSTG) is developing the Space Information Integration and Fusion Experimentation (SPINIFEX) tool as a concept demonstration capability under the Research and Development Space Target Awareness and Response (RED STAR) program. SPINIFEX is designed as an experimentation platform for the ingestion, integration, and fusion of heterogeneous SDA data sources, enabling the systematic exploration of advanced information analytics approaches. Rather than treating data as independent inputs, SPINIFEX provides a coherent framework for transforming disparate datasets into structured, interpretable information products that support analysis and decision advantage.

A central focus of this work is the development and implementation of entity relationship mapping within SPINIFEX. This approach advances SDA analytics beyond object-centric representations toward network-centric models of the space domain. Satellites, spacecraft, ground stations, control nodes, organizations, missions, and infrastructure elements are represented as entities within a unified information model, with explicit relationships formally defined and encoded. These relationships encompass ownership and governance structures, command and control linkages, communication pathways, mission affiliations, operational dependencies, and functional roles within broader system architectures, enabling the representation of space systems as interconnected operational networks rather than isolated platforms.

SPINIFEX employs a centralized SDA data repository that supports the ingestion, fusion, and management of heterogeneous data sources, including orbital data, registry information, mission descriptions, ground infrastructure datasets, organizational and ownership records, and operational metadata. Through structured data transformation and modelling processes, these inputs are converted into relational representations that formally encode inter-entity connections. This enables the construction of dynamic, scalable relationship graphs that capture not only physical space assets, but also the organizational, operational, and functional structures that underpin their employment.

This study presents the conceptual design, data architecture, and implementation methodology for entity relationship mapping within SPINIFEX, and demonstrates how relationship-based representations enable SDA analytics. Representative entity relationship maps generated within the SPINIFEX environment illustrate how raw, heterogeneous data can be transformed into structured information products. These examples show how complex interdependencies and network structures become visible through relational modelling, supporting enhanced situational understanding, anomaly detection, behavioral assessment, and system-level reasoning.

The results demonstrate that relationship-based analytics provide a substantial enhancement over traditional object-centric SDA approaches. By revealing patterns of connectivity, dependency, and control, entity relationship mapping enables deeper insight into system resilience, critical nodes, single points of failure, mission coupling, and potential cascading effects across interconnected infrastructures. This supports not only improved operational awareness, but also strategic assessment, risk analysis, and long-term capability planning.

Overall, this work establishes entity relationship mapping as a foundational enabler for data-driven SDA analytics and decision support. By integrating heterogeneous data sources, SPINIFEX enables a more holistic understanding of the space domain as a complex socio-technical system, rather than a collection of independent objects. This approach provides a robust foundation for next-generation SDA systems, supporting experimentation with data fusion, information analytics, and knowledge representation techniques, and enabling more effective operations in an increasingly complex and contested space environment.