A design for an enhanced data storage in PPH 4.0 involving a time-based graph database

Abstract

The design of Privacy-Preserving Health 4.0 (PPH 4.0) inherently addresses the challenges associated with efficient big data storage and management. To improve data management capabilities in the Healthcare 4.0/5.0 scenario, we aim to present a novel architecture and deployment strategy involving a time-based graph database, integrating the strengths and functionalities of both the time-series database model and the graph database model. In our presented approach, InfluxDB©, ®, representing a time-series database model, and Neo4j©, ®, demonstrating a graph database model, were conjointly utilized to establish a hybrid and effective data management framework in PPH 4.0. With reference to big data scenario, the proposed time-based graph database exhibits optimized approach for relationship-intensive queries, achieving superior performance as compared to traditional databases. Additionally, the proposed time-based graph database in PPH 4.0 offers enhanced flexibility via a dynamic schema that is capable to adapt the evolving data models, high-performance ingestion, and querying with time-stamped data, and efficient storage. Furthermore, the built-in temporal analysis functionalities may enable comprehensive time-based insights, and thereby may facilitate enhanced analytics, and quick data-driven decision-making process. Experimental results from the successful deployment of our presented model demonstrate its potential, and effectiveness for real-world applications in eHealth scenarios.