The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions.
RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge.
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Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web.