Integration of BinRank and HubRank by executing HubRank on MSGs of BinRank generates

This Integration of BinRank and HubRank by executing HubRank on MSGs of BinRank generatespaper is used for high quality, high recall search in databases, and the Web using integration of BinRank and HubRank ,by executing HubRank on MSGs that BinRank generates for improving the performance .BinRank system that employs a hybrid approach where query time can be traded off for preprocessing time and storage. BinRank closely approximates ObjectRank scores by running the same ObjectRank algorithm on a small subgraph, instead of the full data graph. The subgraphs are precomputed offline. The precomputation can be parallelized with linear scalability. For approximating ObjectRank by using Materialized subgraphs (MSGs), which can be precomputed offline to support online querying for a specific query workload, or the entire dictionary. Use of ObjectRank itself to generate MSGs for “bins” of terms.So instead of using the ObjectRank, we use Hub rank algorithm to implement on MSGs. Then the performance of the system is greatly improved resulting the query time can be traded off for preprocessing time and storage. By increasing the relevance of keywords in a bin, we expect the quality of materialized subgraphs, thus the top-k quality and the query time can also be improved