Dynamic Approch to Retrive the Web pages

Dynamic authority-based keyword search algorithms,such as ObjectRank and personalized PageRank, leverage semantic link information to provide high quality, high recall search in databases, and the Web. Conceptually, these algorithms require a querytime PageRank-style iterative computation over the full graph. This computation is too expensive for large graphs, and not feasible at query time. Alternatively, building an index of precomputed results for some or all keywords involves very expensive preprocessing.We introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. We materialize a number of relatively small subsets of the data graph in such a way that any keyword query can be answered by running ObjectRank on only one of the subgraphs. BinRank generates the subgraphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive non-negligible scores. The intuition is that a subgraph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. We demonstrate that BinRank can achieve subsecond query execution time on the English Wikipedia data set, while producing high-quality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 108 edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authority-based search systems have been able to demonstrate. Our experimental evaluation investigates the trade-off between query execution time, quality of the results, and storage requirements of BinRank. INTRODUCTION The PageRank algorithm [1] utilizes the Web graph link structure to assign global importance to Web pages. It works by modeling the behavior of a “random Web surfer”who starts at a random Web page and follows outgoing links with uniform probability. The PageRank score is independent of a keyword query. Recently, dynamic versions of the PageRank algorithm have become popular. They are characterized by a queryspecific choice of the random walk starting points. In particular, two algorithms have got a lot of attention: Personalized PageRank (PPR) for Web graph data sets [2],[3],[4],[5] and ObjectRank for graph-modeled databases [6],[7],[8],[9],[10]. PPR is a modification of PageRank that performs search personalized on a preference set that contains Web pages that a user likes. For a given preference set, PPR performs a very expensive fixpoint iterative computation over the entire Web graph, while it generates personalized search results. Therefore, the issue of scalability of PPR has attracted a lot of attention. ObjectRank extends (personalized) PageRank to perform keyword search in databases. ObjectRank [6] uses a query term posting list as a set of random walk starting points and conducts the walk on the instance graph of the database. The resulting system is well suited for “high recall” search, which exploits different semantic connection paths between objects in highly heterogeneous data sets. ObjectRank has successfully been applied to databases that have social networking components, such as bibliographic data and collaborative product design. However, ObjectRank suffers from the same scalability issues as personalized PageRank, as it requires multiple iterations over all nodes and links of the entire database graph. The original ObjectRank system has two modes: online and offline. The online mode runs the ranking algorithm once the query is received, which takes too longon large Muthe V Satya Nagaraju et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (6) , 2011, 2541-2547.