Next Article in Journal
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model
Previous Article in Journal
Uncertainty Quantification for Space Situational Awareness and Traffic Management
Open AccessArticle

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

1
Insight Centre for Data Analytics, National University of Ireland Galway, H91 TK33 Galway, Ireland
2
Information Technology Department, Hue University, Hue 530000, Vietnam
3
Confirm Centre for Smart Manufacturing and Insight Centre for Data Analytics, National University of Ireland Galway, H91 TK33 Galway, Ireland
4
Open Distributed Systems, Technical University of Berlin, 10587 Berlin, Germany
*
Author to whom correspondence should be addressed.
This paper is an extension version of the conference paper: Nguyen Mau Quoc, H; Le Phuoc, D.: “An elastic and scalable spatiotemporal query processing for linked sensor data”, in proceedings of the 11th International Conference on Semantic Systems, Vienna, Austria, 16–17 September 2015.
Sensors 2019, 19(20), 4362; https://doi.org/10.3390/s19204362
Received: 22 July 2019 / Revised: 2 October 2019 / Accepted: 4 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context. View Full-Text
Keywords: internet of things; graph of things; linked stream data; linked sensor data; semantic web; sensor network; spatial data; temporal RDF; RDF stores internet of things; graph of things; linked stream data; linked sensor data; semantic web; sensor network; spatial data; temporal RDF; RDF stores
Show Figures

Figure 1

MDPI and ACS Style

Nguyen Mau Quoc, H.; Serrano, M.; Mau Nguyen, H.; G. Breslin, J.; Le-Phuoc, D. EAGLE—A Scalable Query Processing Engine for Linked Sensor Data. Sensors 2019, 19, 4362.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop