k Nearest Neighbor (kNN) query has received considerable attention from the database and information retrieval communities. The applications of kNN query are widely, not only in spatio-temporal database but also in many areas. The past studies of kNN query processing did not consider the case that the server may receive multiple kNN queries at a time. Their algorithms process queries independently. Thus, server will busy with continuously re-accessing database to obtain the data that already

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... uired. It results in wasting I/O cost and degrading the performance of whole system. In this paper, we focus on this problem and propose an algorithm that namely Multiple kNN Search. The main idea of this problem is "information sharing" strategy that server re-uses the query results of previously executed queries for efficiently processing subsequent queries. We conduct a comprehensive set of experiments to analyze the performance of Multiple kNN Search and compare it with Best-First Search (BFS) algorithm. Empirical studies indicate that the performance of Multiple kNN Search outperforms BFS, achieves lower I/O cost and less running time. Index Terms-k Nearest Neighbor, Multiple kNN query, spatio-temporal database, information sharing strategy, query processing.