doi: 10.18178/ijiet.2017.7.5.900
Continuous Mutual Nearest Neighbour Processing on Moving Objects in Spatiotemporal Datasets
Shiva Ghorbani
,
Mohammad Hadi Mobini ,
Behrouz Minaei-Bidgoli
Abstract
This paper proposed a new algorithm for answering a novel kind of nearest neighbour search, that is, continuous mutual nearest neighbour (CMNN) search. In this kind of query, by providing a set of objects O and a query object q, CMNN continuously returns the set of objects from O, which is among the k1 nearest neighbours of q; meanwhile, q is one of their k2 nearest neighbours. CMNN queries are important in many applications such as decision making, pattern recognition and although it is useful in service providing systems, such as police patrol, taxi drivers, mobile car repairs and so forth. In this paper, we have proposed the first work for handling CMNN queries efficiently, without any assumption on object movements. The most important feature of this work is incremental evaluation and scalability. Utilizing an incremental evaluation technique led to a significant decrease in processing time.
Keywords
- Moving objects
- nearest neighbor
- query processing
- spatio-temporal
How to Cite
Shiva Ghorbani, Mohammad Hadi Mobini, and Behrouz Minaei-Bidgoli, "Continuous Mutual Nearest Neighbour Processing on Moving Objects in Spatiotemporal Datasets," International Journal of Information and Education Technology, vol. 7, no. 5, pp. 392-399, 2017. https://doi.org/10.18178/ijiet.2017.7.5.900
Copyright & License
Copyright © 2017 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).