Research Article
Cloud-Assisted Spatio-Textual k Nearest Neighbor Joins in Sensor Networks
@INPROCEEDINGS{10.4108/icst.iniscom.2015.258321, author={Mingyang Yang and Long Zheng and Yanchao Lu and Minyi Guo and Jie Li}, title={Cloud-Assisted Spatio-Textual k Nearest Neighbor Joins in Sensor Networks}, proceedings={3rd International Workshop on Software Defined Sensor Networks}, publisher={ICST}, proceedings_a={SDSN}, year={2015}, month={4}, keywords={sensor networks cloud computing distributed computing mapreduce k nearest neighbor join}, doi={10.4108/icst.iniscom.2015.258321} }- Mingyang Yang
Long Zheng
Yanchao Lu
Minyi Guo
Jie Li
Year: 2015
Cloud-Assisted Spatio-Textual k Nearest Neighbor Joins in Sensor Networks
SDSN
ICST
DOI: 10.4108/icst.iniscom.2015.258321
Abstract
k nearest neighbors (kNN) query is an important problem in a variety of sensor network applications. Traditionally, we handle this problem with a single query processing approach, which just considers the location information. It usually neglects the other information such as temperature, humidity, pressure, etc. In order to overcome the defect of the traditional approaches, we investigate the problem from a new perspective and desire to solve a more interesting problem called spatio-textual k nearest neighbor join (ST-kNNJ). It searches text-similar and k-nearest sensors to a query set containing more than one query point. With the help of cloud computing, ST-kNNJ can be processed in distributed computational environment to gain better processing capability and response efficiency. In this paper, we generalize the problem of ST-kNNJ and propose our approaches to it. And we can deal with large-scale data when using MapReduce framework. Evaluation results show that our approach achieve better performance in comparison with the naive approach.