Additionally, we discuss future trends and new lines of research related to this field. We broadly survey 62 flash-aware indexes for various data types, analyze the main techniques they employ, and comment on their main advantages and disadvantages, aiming to provide a systematic and valuable resource for researchers working on algorithm design and index development for SSDs. In this work, we present a concise overview of the SSD technology and the challenges it poses. These peculiarities of SSDs dictate the refactoring or even the reinvention of the indexing techniques that have been designed primarily for HDDs. However, treating SSDs as simply another category of block devices ignores their idiosyncrasies, like erase-before-write, wear-out and asymmetric read/write, and may lead to poor performance. In the recent years, solid-state drives (SSDs), based on NAND flash technology, started replacing magnetic disks due to their appealing characteristics: high throughput/low latency, shock resistance, absence of mechanical parts, low power consumption. Indexing has been actively and extensively investigated in DBMSes equipped with hard disk drives (HDDs). Indexes are special purpose data structures, designed to facilitate and speed up the access to the contents of a file. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples.
With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. 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.
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