An elastic and scalable spatiotemporal query processing for linked sensor data

Abstract

Recently, many approaches have been proposed to manage sensor data using Semantic Web technologies for effective heterogeneous data integration. However, our research survey revealed that these solutions primarily focused on semantic relationships and still paid less attention to its temporal-spatial correlation. Most semantic approaches do not have spatiotemporal support. Some of them have served limitations on providing full spatiotemporal support but have poor performance for complex spatiotemporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, a challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this paper, we propose a spatiotemporal query engine for sensor data based on Linked Data model. The ultimate goal of our approach is to provide an elastic and scalable system which allows fast searching and analysis on the relationships of space, time and semantic in sensor data. We also introduce a set of new query operators in order to support spatiotemporal computing in linked sensor data context.