RDF4Led

Le-Tuan, Anh; Hayes, Conor; Wylot, Marcin; Phuoc, Danh Le

doi:10.1145/3277593.3277600

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The Physical Layer can be viewed as a key-value store. RDF graphs are compressed into numerous RDF molecules, which are compact sorted lists of properties and objects related to one subject as described in [19]. Therefore, storage space could be greatly saved by avoiding redundant storage of subject values.…”

Section: Distributed Rdf Storage Using P-grid Model 31 Design Of Dist...mentioning

confidence: 99%

“…Because of its natural behaviour, the hash function is suitable for key-value structures. The encoded RDF triples are indexed with three index layouts (SPO, POS, OSP) and are stored with a Storage Manager that employs a two-layer index for each layout as presented in [19]. SPARQL queries are registered on the system via a Query Handler and are compiled with a Query Compiler.…”

Section: System Architecture and Implementationmentioning

confidence: 99%

“…As the decentralised integration paradigm fits better with the distributed nature of the autonomous deployment of smart IoT devices, RDF4Led [19] was proposed to move RDF data processing to IoT edge devices. The RDF engine consists of an RDF storage and SPARQL processor tailored for a lightweight edge machine to store and query RDF data.…”

mentioning

confidence: 99%

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Building a P2P RDF Store for Edge Devices

Guo,

Le-Tuan,

Le-Phuoc

2023

Proceedings of the International Conference on the Internet of Things

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The Semantic Web technologies have been used in the Internet of Things (IoT) to facilitate data interoperability and address data heterogeneity issues. The Resource Description Framework (RDF) model is employed in the integration of IoT data, with RDF engines serving as gateways for semantic integration. However, storing and querying RDF data obtained from distributed sources across a dynamic network of edge devices presents a challenging task. The distributed nature of the edge shares similarities with Peer-to-Peer (P2P) systems. These similarities include attributes like node heterogeneity, limited availability, and resources. The nodes primarily undertake tasks related to data storage and processing. Therefore, the P2P models appear to present an attractive approach for constructing distributed RDF stores. Based on P-Grid, a data indexing mechanism for load balancing and range query processing in P2P systems, this paper proposes a design for storing and sharing RDF data on P2P networks of low-cost edge devices. Our design aims to integrate both P-Grid and an edge-based RDF storage solution, RDF4Led for building an P2P RDF engine. This integration can maintain RDF data access and query processing while scaling with increasing data and network size. We demonstrated the scaling behavior of our implementation on a P2P network, involving up to 16 nodes of Raspberry Pi 4 devices.

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Section: Distributed Rdf Storage Using P-grid Model 31 Design Of Dist...mentioning

confidence: 99%

Section: System Architecture and Implementationmentioning

confidence: 99%

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Building a P2P RDF Store for Edge Devices

Guo,

Le-Tuan,

Le-Phuoc

2023

Proceedings of the International Conference on the Internet of Things

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“…Slider [9] is an incremental reasoner optimised in memory and processing footprint. RDF4LED [40] is a lightweight RDF engine, which comprises of RDF storage and a SPARQL processor, for small query operations in lightweight edge devices. Devices on the WoT generate and consume highly dynamic data.…”

Section: Distributed and Embedded Reasoning In The Edgementioning

confidence: 99%

Semantics in the Edge: Sensors and actuators in the Web of Linked Data and Things

Cena

Haller

Lefrançois

2020

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“…Edge computing is pushing the computation closer to the data sources, e.g. a resource-constrained edge device can efficiently store and process 30 million of RDF triples with 80 MB of RAM [6]. Since stream data is often generated in a distributed fashion, these decentralized computing paradigms are suitable and desirable choices to develop stream reasoning solutions.…”

mentioning

confidence: 99%