• Aucun résultat trouvé

A Semantically Enriched Hypercat-enabled Internet of Things Data Hub

N/A
N/A
Protected

Academic year: 2022

Partager "A Semantically Enriched Hypercat-enabled Internet of Things Data Hub"

Copied!
2
0
0

Texte intégral

(1)

A Semantically Enriched Hypercat-enabled Internet of Things Data Hub

Ilias Tachmazidis1, John Davies2, Sotiris Batsakis1, Alistair Duke2, Grigoris Antoniou1, and Sandra Stincic Clarke2

1 University of Huddersfield, Huddersfield, UK

2 British Telecommunications, Ipswich, UK

Abstract. A huge amount of information is generated from the rapidly increasing number of sensor networks and smart devices, generating and publishing information in multiple formats, thus highlighting interop- erability as one of the key prerequisites for the success of the Internet of Things (IoT). TheBT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we present a semantic enrichment of the data hub, using a number of widely-used Semantic Web standards and tools.

1 BT Hypercat Data Hub

Developing successful IoT solutions requires the collection and mainte- nance of a wide range of heterogeneous IoT data. In this work, we describe the basic components of the BT Hypercat Data Hub, which aggregates and catalogues mulitple IoT data sources and exposes them via a uni- form RESTful API. For more details readers are referred to [1]. Figure 1 presents a high level hierarchy of the developed system, more specifically:

– TheBT Hypercat Ontology enables the publication of an RDF-based Hypercat catalogue [2] as well as the translation of data stored in a relational database into RDF format.

– RDF Adapters provide internally stored data in N-Triples format fol- lowing a systematic generation of URIs.

– ASPARQL to SQLendpoint enables the dynamic translation of SPARQL queries into SQL queries, using Ontop3.

– The BT SPARQL Endpoint queries internally available SPARQL to SQLendpoints and combines SPARQL results, using Apache Jena4. – Federated querying is enabled by providing a Jena endpoint that al-

lows federated queries over theBT SPARQL Endpoint and SPARQL endpoints that are available through the Linked Open Data cloud.

3 http://ontop.inf.unibz.it/

4 https://jena.apache.org/index.html

(2)

Fig. 1.High Level Hierarchy.

The system has been successfully deployed as part of two use cases.

TheSimplifAI project is aimed at urban traffic management and control in order to reduce traffic and improve air quality. TheBT Hypercat Data Hub was utilized for automated semantic enrichment of imported IoT data. In addition, City Concierge is a use case of the CityVerve project aiming to increase uptake of walking and cycling as a preferred travel mode in Greater Manchester. The data hub provides the required infras- tructure and functionality in order to enable the City Concierge through the aggregation and exposure of city IoT information.

2 Conclusion

The semantic enrichment of the BT Hypercat Data Hub has been pre- sented. Future work includes further semantic enrichment by enabling GeoSPARQL queries. In addition, spatiotemporal reasoning is a promi- nent direction that could provide richer knowledge by combining data coming from both the BT Hypercat Data Hub and the LOD cloud.

References

1. Ilias Tachmazidis, Sotiris Batsakis, John Davies, Alistair Duke, Mauro Vallati, Grig- oris Antoniou, and Sandra Stincic Clarke. A hypercat-enabled semantic internet of things data hub. In The Semantic Web - 14th International Conference, ESWC 2017, Portoroˇz, Slovenia, May 28 - June 1, 2017, Proceedings, Part II, pages 125–

137, 2017.

2. Ilias Tachmazidis, John Davies, Sotiris Batsakis, Grigoris Antoniou, Alistair Duke, and Sandra Stincic Clarke. Hypercat RDF: Semantic Enrichment for IoT. InSe- mantic Technology - 6th Joint International Conference, JIST 2016, Singapore, Sin- gapore, November 2-4, 2016, Revised Selected Papers, pages 273–286, 2016.

Références

Documents relatifs

SPARQture, the tool described in this paper, automates this process and renders the structure of a particular RDF data available behind some SPARQL endpoint using a number of visu-

When a query prepared by the client might require a long execution time, a standard SPARQL endpoint implementation will try to execute the query with lots of cost to return answers..

In this work, we present a series of optimizations applied on the BT Hypercat Data Hub that enabled scalable SPARQL query answering over relational databases and an access

For example, IQ-rewritability means rewritability of an OMQ from (ALCI, UCQ) into an OMQ from (ALCI , IQ) with the IQ formu- lated in ALCI, and PUBGP-rewritability means

Linked Data Visualization Wizard [1] is an on-line tool 11 detecting presence of sev- eral types of information inside a SPARQL endpoint, including temporal information,

Since the new query functionality offered by SPARQL-LD (allowing to query any HTTP resource containing RDF data) can lead to queries with large num- ber of service patterns which

We use SPARQL conjunctive queries as views that describe sources contents. As in the Bucket Algorithm [20], relevant sources are selected per query subgoal, i.e., triple pattern.

However, the majority of SPARQL implemen- tations require the data to be available in advance, i.e., to exist in main memory or in a RDF repository (accessible through a SPARQL