On processing continuous frequent K-N-match queries for dynamic data over networked data sources |
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Authors: | Shih-Chuan Chiu Jiun-Long Huang Jen-He Huang |
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Affiliation: | 1. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC
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Abstract: | Similarity search is one of the critical issues in many applications. When using all attributes of objects to determine their similarity, most prior similarity search algorithms are easily influenced by a few attributes with high dissimilarity. The frequent k-n-match query is proposed to overcome the above problem. However, the prior algorithm to process frequent k-n-match queries is designed for static data, whose attributes are fixed, and is not suitable for dynamic data. Thus, we propose in this paper two schemes to process continuous frequent k-n-match queries over dynamic data. First, the concept of safe region is proposed and four formulae are devised to compute safe regions. Then, scheme CFKNMatchAD-C is developed to speed up the process of continuous frequent k-n-match queries by utilizing safe regions to avoid unnecessary query re-evaluations. To reduce the amount of data transmitted by networked data sources, scheme CFKNMatchAD-C also uses safe regions to eliminate transmissions of unnecessary data updates which will not affect the results of queries. Moreover, for large-scale environments, we further propose scheme CFKNMatchAD-D by extending scheme CFKMatchAD-C to employ multiple servers to process continuous frequent k-n-match queries. Experimental results show that scheme CFKNMatchAD-C and scheme CFKNMatchAD-D outperform the prior algorithm in terms of average response time and the amount of produced network traffic. |
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