The Moving K Diversified Nearest Neighbor Query
The Moving K Diversified Nearest Neighbor Query
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DOI：
10.1109/tkde.2016.2593464
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Yu Gu;Guanli Liu;Jianzhong Qi;Hongfei Xu;Ge Yu;Rui Zhang
中科院分区：
文献类型：
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作者：
Yu Gu;Guanli Liu;Jianzhong Qi;Hongfei Xu;Ge Yu;Rui Zhang
我们研究了连续空间查询处理中的结果多样化，并提出了一种新的查询类型，移动k多样化最近邻查询（MkDNN）。给定一个移动的查询对象，MkDNN查询连续地维护查询对象的k个多样化的最近邻居。这里，最近邻的多样性是根据最近邻之间的距离来定义的。我们提出了一种算法，以保持增量的k多样化的最近邻居，以减少连续查询处理的成本。我们进一步提出了两个近似算法，以获得更高的查询效率与精度界限。我们验证了所提出的算法的有效性和效率的经验。实验结果证实了所提算法的优越性。
We study result diversification in continuous spatial query processing and formulate a new type of queries, the moving k diversified nearest neighbor query (MkDNN). Given a moving query object, an MkDNN query maintains continuously the k diversified nearest neighbors of the query object. Here, how diversified the nearest neighbors are is defined on the distance between the nearest neighbors. We propose an algorithm to maintain incrementally the k diversified nearest neighbors to reduce the costs of continuous query processing. We further propose two approximate algorithms to obtain even higher query efficiency with precision bounds. We verify the effectiveness and efficiency of the proposed algorithms empirically. The results confirm the superiority of the proposed algorithms.