TAILIEUCHUNG - A new approach of an efficient multiple keywords search method using extended skip graph
In order to narrow down the scope of target nodes sometimes using it, multiple keywords can be efficiently executed. In order to confirm the effectiveness of the proposed method, we conducted an experiment by simulation and we will also describe the results. | ISSN:2249-5789 Upinder Kaur et al, International Journal of Computer Science & Communication Networks,Vol 6(6),237-247 A New Approach of an Efficient Multiple Keywords Search Method using Extended Skip Graph 1. Upinder Kaur, Research Scholar, Dept. of Computer Science and Applications, KUK. 2. Dr. Pushpa Rani Suri, Professor, Dept. of Computer Science and Applications, KUK. Abstract: It is difficult to efficiently search data including a plurality of keywords at the same time. In order to solve this problem, this paper proposes an efficient search method using Bloom filter. In Bloom filter, all keys included in the data to be searched, it becomes possible to compactly store words. Bloom filter of each node, the contents are aggregated and held by an algorithm based on extended Skip graph. In order to narrow down the scope of target nodes sometimes using it, multiple keywords can be efficiently executed. In order to confirm the effectiveness of the proposed method, we conducted an experiment by simulation and we will also describe the results. 1. INTRODUCTION: We propose to apply bloom filter with skip graph to realize the searching function while at the same time protecting the user privacy. A bloom filter (BF) [1-5] is an excellent compact data structure that can succinctly represent a data set in order to support membership queries, and filter out effectively any element that does not belong to the set. With the features of simplicity, ease of use, hardware support, and excellent performance, bloom filters have broad applications. Particularly, bloom filters can work efficiently in a distributed system where information and data need to be exchanged among different entities. The straight-forward BF approach, however, cannot work well in a practical social network. The social networks are often dynamic, and user IJCSCN
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