Tree Based Fast Similarity Query Search Indexing on Outsourced Cloud Data Streams

A Cloud may be seen as flexible computing infrastructure comprising of many nodes that support several concurrent end users. To fully harness the power of the Cloud, efficient data query processing has to be ascertained. This work provides extra functionalities on cloud data query processing, a method called, Hybrid Tree Fast Similarity Query (HT-FSQS) Search is presented. The Hybrid Tree structure used in HT-FSQS consists of E-tree and R+tree for balancing the load and performing similarity search. In addition, we articulate performance optimization mechanisms for our method by indexing quasi data objects to improve the quality of similarity search using R+tree mechanism. Fast Similarity Query Search indexing build cloud data streams for handling different types of user queries and produce the result with lesser computational time. Fast Similarity Query Search uses inter-intra bin pruning technique, where it resolves the data more similar to user query. E-R+tree FSQ method branch and bound search eliminates certain bins from consideration, speeding up the indexing operation. The experiment results demonstrate that the Hybrid Tree Fast Similarity Query (HT-FSQS) Search achieve significant performance gains in terms of computation time, quality of similarity search and load balance factor in comparison with non-indexing approaches.