The SSN ontology of the W3C semantic sensor network incubator group

Compton, Michael; Barnaghi, Payam; Bermúdez, Luis; García‐Castro, Raúl; Corcho, Óscar; Cox, Simon J.; Graybeal, John; Hauswirth, Manfred; Henson, Cory; Herzog, Arthur; Huang, Vincent; Janowicz, Krzysztof; Kelsey, W. David; Phuoc, Danh Le; Lefort, Laurent; Leggieri, Myriam; Neuhaus, Holger; Nikolov, Andriy; Page, Kevin; Passant, Alexandre; Sheth, Amit P.; Taylor, Kerry

doi:10.1016/j.websem.2012.05.003

Cited by 1,094 publications

(305 citation statements)

References 13 publications

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“…Finally, we compare StreamQR with TrOWL [23], a state-of-the-art stream reasoner, which provides incremental reasoning over ontology streams, although not targeted towards query answering. We used a modified version of the SRBench [28] benchmark queries, as well as an ontology based on AWS (Ontology for Meteorological sensors 4 ), which extends the W3C SSN ontology [11]. The ontology describes sensors, observations, features of interest, ans other weatherrelated concepts.…”

Section: Discussionmentioning

confidence: 99%

Query Rewriting in RDF Stream Processing

Calbimonte

Mora

Corcho

2016

The Semantic Web. Latest Advances and New Domains

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Abstract. Querying and reasoning over RDF streams are two increasingly relevant areas in the broader scope of processing structured data on the Web. While RDF Stream Processing (RSP) has focused so far on extending SPARQL for continuous query and event processing, stream reasoning has concentrated on ontology evolution and incremental materialization. In this paper we propose a different approach for querying RDF streams over ontologies, based on the combination of query rewriting and stream processing. We show that it is possible to rewrite continuous queries over streams of RDF data, while maintaining efficiency for a wide range of scenarios. We provide a detailed description of our approach, as well as an implementation, StreamQR, which is based on the kyrie rewriter, and can be coupled with a native RSP engine, namely CQELS. Finally, we show empirical evidence of the performance of StreamQR in a series of experiments based on the SRBench query set.

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Section: Discussionmentioning

confidence: 99%

Query Rewriting in RDF Stream Processing

Calbimonte

Mora

Corcho

2016

The Semantic Web. Latest Advances and New Domains

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“…A common way of representing Linked Data is with the Resource Description Format (RDF) where the use and referencing of common identifiers and ontologies helps to integrate data. Furthermore, sensor ontologies like the Semantic Sensor Ontology (SSN) [9] have been developed for Linked Data which encourages providing machine-interpretable descriptions within RDF to describe what data represents, where it originates from, how it can be related to its surroundings, who is providing it, and what its attributes are e.g. a unit of measure for each sensor reading, its sensor platform and location.…”

Section: Linked Data For Interoperabilitymentioning

confidence: 99%

“…Hence, Linked Data provides IoT interoperability as 1) RDF graphs allow structured data to be interconnected with IRIs providing common identifiers that can be referenced across sources, 2) SPARQL provides a means for semantically querying RDF graphs and 3) there exist many ontologies like the SSN Ontology [9] that provide a reference and model for organising sensor data.…”

Section: Linked Data For Interoperabilitymentioning

confidence: 99%

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

2017

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Abstract. Fog computing is an emerging technology for the Internet of Things (IoT) that aims to support processing on resource-constrained distributed nodes in between the sensors and actuators on the ground and compute clusters in the cloud. Fog Computing benefits from low latency, location awareness, mobility, wide-spread deployment and geographical distribution at the edge of the network. However, there is a need to investigate, optimise for and measure the performance, scalability and interoperability of resource-constrained Fog nodes running real-time applications and queries on streaming IoT data before we can realise these benefits. With Eywa, a novel Fog Computing infrastructure, we 1) formally define and implement a means of distribution and control of query workload with an inverse publish-subscribe and push mechanism, 2) show how data can be integrated and made interoperable through organising data as Linked Data in the Resource Description Format (RDF), 3) test if we can improve RDF Stream Processing query performance and scalability over state-of-the-art engines with our approach to query translation and distribution for a published IoT benchmark on resource-constrained nodes and 4) position Fog Computing within the Internet of the Future.

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“…using standardized semantics or ontological data structures as depicted in Fig. 1 [16][17][18][19]). The need for such a convergence of IoT ecosystems has been recognised from the early advent of the impact of IoT diversity in many environments including the foreseen growth in Smart Cities [18].…”

Section: Formulations Of the Concepts For Novel Iot Communication Modelsmentioning

confidence: 99%

“…However, IoT bring a physical, spatial, timely, use-related and connectivity factors in the equation when thinking about the search and discovery. As shown in [16], some of these factors have been recognised as key descriptions of sensors from the early advent of the technology and have in fact been the triggers for development of Semantic Sensor Web (SSW) using ontology languages such as OWL/RDF that are being perfected by international initiatives and working groups [17,19]. Much of the SSW ideas have gone into complex environmental descriptions and application-level search and processes.…”

Section: Different Fundamentals Of Search and Discovery Of Iot Devicementioning

confidence: 99%

Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

Mihailović

2016

Wireless Pers Commun

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There is an ongoing enormous expansion of Internet of Things devices and services in everyday life, notably in novel large scale urban environments called Smart Cities. There, availability and uses of Internet of Things by end users and businesses is mainly palpable subject to prior knowledge of the relevant providers and use of dedicated applications that are associated with them. This current reality can be largely ascribed to the property of ''verticality'' of autonomous Internet of Things eco-systems in Smart Cities, where Internet of Things devices (e.g. sensor nodes) are connected over a communication infrastructure to service-cloud platforms that deliver and process data that is then presented at the applications level. This paper explains possibilities for revolutionary changes needed towards liberalising deployment and visibility of IoT services and data associated with them. It advocates a conceptual approach termed ''horizontal networking for Internet of Things'' facilitating a more open and generic presence of Internet of Things through the proposed Internet of Things identification meta-data. The vision is built on needed novel practical features in the current communication setups. The features comprise combinations of the opportunistic and near-match search and discovery model, Internet of Things identification meta-data also reflecting the physical and network-based dimensions of devices' locations, novel routing and data flow models emerging via InformationCentric Networking and changes required in the elements of the current telecommunication infrastructure and the Internet.

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The SSN ontology of the W3C semantic sensor network incubator group

Cited by 1,094 publications

References 13 publications

Query Rewriting in RDF Stream Processing

Query Rewriting in RDF Stream Processing

Ewya: An Interoperable Fog Computing Infrastructure with RDF Stream Processing

Liberalising Deployment of Internet of Things Devices and Services in Large Scale Environments

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