Speeding Up Similarity Queries over Large Chinese Calligraphic Character Databases Using Data Grid

Abstract

This paper proposes a novel data-grid-based k nearest neighbor query over large Chinese calligraphic character databases, which can significantly speed up the retrieval efficiency. Three steps are made. Firstly, when a user submits a query request to a query node, a process of character set reduction is performed using iDistance index in different data nodes, followed by sending the candidate characters to the executing nodes through a package-based transfer technique. Secondly, a refinement process of the candidate characters is conducted in the executing nodes in parallel to get the answer set. Finally, the answer set is transferred to the query node. The proposed method incorporates a uniform-start- distance-based character data allocation policy and character reduction algorithm. The analysis and experimental results show that the performance of the algorithm is effective in minimizing the response time by decreasing network transfer cost and increasing the parallelism of I/O and CPU.