Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10881
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Main Title: Pushing the Scalability of RDF Engines on IoT Edge Devices
Author(s): Le-Tuan, Anh
Hayes , Conor
Hauswirth, Manfred
Le-Phuoc, Danh
Type: Article
Language Code: en
Abstract: Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. 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. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. 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. 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. 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. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. 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. 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. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.
URI: https://depositonce.tu-berlin.de/handle/11303/12001
http://dx.doi.org/10.14279/depositonce-10881
Issue Date: 14-May-2020
Date Available: 18-Nov-2020
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): Internet of Things
edge device
the semantic web
RDF engine
Sponsor/Funder: BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data
BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data
EC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTER
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Sensors
Publisher: MDPI
Publisher Place: Basel
Volume: 20
Issue: 10
Article Number: 2788
Publisher DOI: 10.3390/s20102788
EISSN: 1424-8220
Appears in Collections:FG Verteilte offene Systeme » Publications

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