Optimization method of elastic aggregation nearest neighbor query G tree on road network

Abstract

The invention discloses an optimization method of an elastic aggregation nearest neighbor query G tree on a road network. The method comprises the following steps: one, establishing G tree index; two,defining and initializing; three, ending if the queue is empty, or exiting to obtain x, and entering the step four; four, if x is leaf node, computing gPhi(v, Q) (including initialization: judging whether D is less than PhiM and whether the queue is empty; exiting to obtain (dis, e), and judging whether e is the point on the road network) by adopting the optimization method for all points v in the x, and updating the final result, traversing to back to the third step; or entering the fifth step; five, traversing the sub-node c of x, computing the minimum possible distance to c form all pointsin Q, obtaining the maximum value max or sum of the front PhiM distance, and recording as Tau; six, if the Tau is less than r*, queueing the children node of the c and returning to step three; if Tauis greater than or equal to r*, ending the operation. Through the method disclosed by the invention, the efficiency of the gPhi can be effectively improved, thereby improving the query speed, and reducing the cost.

Description

technical field [0001] The invention belongs to the field of computers, and in particular relates to a query method for a spatial database, in particular to an optimization method for elastic aggregation nearest neighbor query G-trees on a road network. Background technique [0002] Aggregate nearest neighbor query (hereinafter referred to as ANN) is a classic query in spatial databases and has a wide range of application scenarios, such as location-based services. Given a set of query points Q, ANN finds a point in the data point set V such that the aggregated distance from this point to all points in Q is the smallest. This aggregation function is usually max or sum. ANN problems have been discussed in Euclidean spaces [see D. Papadias, Q. Shen, Y. Tao, and K. Mouratidis, “Group nearest neighbor queries,” in Data Engineering, 2004. Proceedings. 20th International Conference on. IEEE, 2004, pp. 301–312.] and road networks [see D. Papadias, Q. Shen, Y. Tao, and K. Mouratid...

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Problems solved by technology

[D. Papadias, Q. Shen, Y. Tao, and K. Mouratidis, “Group nearest neighbor queries,” in Data Engineering, 2004. Proceedings. 20th International Conference on. IEEE, 2004, pp.301–312.] The IER algorithm relies on the R tree, but the R tree does not perform well on the road network

[D.Yan, Z.Zhao, and W. Ng, "Efficient algorithms for finding optimal meeting point on road networks," Proceedings of the VLDB Endowment, vol.4, no.11, 2011.] used the convex hull method to Impossible point pruning, but its scalability is not good

[M. Safar, "Group k-nearest neighbors queries in spatial network databases," Journal of geographic systems, vol.10, no.4, pp.407–416, 2008.] [L.Zhu, Y.Jing, W. Sun, D.Mao, and P.Liu, "Voronoi-based aggregate nearest neighbor query processing in road networks," in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2010, pp.518–521.] both used Voronoi diagrams partition the road network, but they often lead to unbalanced division, which leads to inefficiency

Additionally, due to the newly added parameter FANN results will be harder to find

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