EAGLE-A Scalable Query Processing Engine for Linked Sensor Data.

Hoan Nguyen Mau Quoc,Danh Le-Phuoc,Han Mau Nguyen,John G Breslin,Martin Serrano

doi:10.3390/s19204362

Abstract

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.

Highlights

The internet of things (IoT) is the network of physical objects embedded with sensors that are enabling real-time observations about the world as it happens
Framework (RDF) data model that has been widely used for representing web data, to connect dynamic data streams generated from IoT devices, e.g., sensor readings, with any relevant knowledge base, Sensors 2019, 19, 4362; doi:10.3390/s19204362
We evaluated EAGLE’s performance with respect to data loading throughput on our physical setup and compared it to the state-of-the-art systems

Summary

Introduction

The internet of things (IoT) is the network of physical objects embedded with sensors that are enabling real-time observations about the world as it happens. An extensive amount of ontologies have been proposed, which aim to address the challenge of modeling a sensor network and its data, and to tackle the heterogeneity problems associated with the hardware, software, and the data management aspect of sensors. They provide a means to semantically describe the sensor networks, the sensing devices, the sensor data, and enable sensor data fusion. Sensor Web Enablement (OGC SWE) working group [6] They have specified a number of standards that define formats for sensor data and metadata as well as sensor service interfaces. These standards allow the integration of sensor and sensor networks into the web, in what is called the sensor web

Results

Discussion

Conclusion