Advanced Racing.ai is Integrating Motec with Equitus AI's KGNN (Knowledge Graph Neural Network). Auto Racing is trapped in a spiral of increasing costs and uninspired racing. Fans and automobile manufacturers struggle to find the balance between costs and balance of performance. Relative performance requires making upgrades within a specified rule book. In F1 300 analogue sensors are used to monitor performance. The cost is high and the benefits are small. By introducing a Knowledge Graph Neural Network, the ability to make adjustments efficiently is enhanced. Fusion Racing Platform platform can offer several key benefits:
- Real-Time Data Integration and Unification: Motec's capabilities in real-time data ingestion and processing can be combined with KGNN's ability to dynamically incorporate new data types without a predefined schema
- Advanced Semantic Reasoning and Insights :KGNN's advanced semantic reasoning capabilities and dynamic learning and inference can uncover hidden insights and relationships within the data ingested by Motec
- Interoperable Knowledge Creation: KGNN produces coherent and interoperable knowledge by adhering to standard data formats like RDF and OWL, and utilizing query languages like SPARQL
- Flexible and Agnostic Deployment: As a flexible middleware platform, KGNN can be deployed both on-premises and in the cloud, making it compatible with Motec's deployment options
- Data Democratization and Accessibility: The integration can enable the democratization of data and analytics across an organization
- Data Security and Sovereignty: Equitus AI's focus on data security and sovereignty, grounded in national security principles
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