RDF Data Query and Management Method Based on HBase and Structure Index in Railway Sensor Application

Abstract

Railway dangerous goods tracing is a typical application of the sensor network. Application correlation among the sensor, carriage and train is a graph relationship which can be described by using RDF frameworks. It requires data management system to manage a large scale of ever-increasing RDF data, and support semantic access for monitoring the safety state of the environment inside the carriage. For these problems, this paper proposes RDF data query and management method based on HBase and structure index, and optimization method of query engine. The method is enforced by rewriting SPARQL statements according of correlation degree between them, and at querying time, "structure-level" index is used to identify the groups of RDF data, then the "data-level" data matching utilizes the proposed scalable storage mechanism based on hash-oriented multiple table partition of data entity class. As shown in our experiments, our approach can effectively reduce the semantic query time, enhance storage scalability and effective support multi-criteria query of sensor data.