Approximate search algorithm for aggregate k-nearest neighbour queries on remote spatial databases
Hideki Sato, Ryoichi Narita · International Journal of Knowledge and Web Intelligence · 2013
Searching Aggregate k–Nearest Neighbour (k–ANN) queries on remote spatial databases suffers from a large amount of communication. In order to overcome the difficulty, RQP–M algorithm for efficiently searching k–ANN query results is proposed in this paper. It refines query results originally searched by RQP–S with subsequent k–NN queries, whose query points are chosen among vertices of a regular polygon inscribed in a circle searched previously. Experimental results show that precision of sum k–NN query results is over 0.95 and Number of Requests (NOR) is at most 4.0. On the other hand, precision of max k–NN query results is over 0.95 and NOR is at most 5.6. RQP–M brings 0.04–0.20 increase in PRECISION of sum k–NN query results and over 0.40 increase in that of max k–NN query results, respectively, in comparison with RQP–S.