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.
Index Terms—Moving objects, nearest neighbor, query processing, spatio-temporal.
Shiva Ghorbani is with School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran (e-mail: email@example.com).
Mohammad Hadi Mobini is with the Department of Computer Engineering, Sharif University of Technology, Tehran, Iran (e-mail: firstname.lastname@example.org).
Behrouz Minaei-Bidgoli is with the School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran (e-mail: email@example.com).
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.