Abstract
Graphs are used in many disciplines, from communication networks, biological, social networks including maths and other fields of science. This is the latest and most important field of computer science today. In this research, the authors have worked on the materialization to improve the query response of graph data. The large graph dataset have been divided into two categories; one contains the topological data and other contains the aggregate data and both are accessed via a PAM (Predicate Aggregate Materialization) engine which plays an intermediary role. PAM engine stores the query results and it checks whether the query is new or already processed every time the query appears. If it is found already processed than it just get the results which are materialized and if it finds a new query than it goes for the extraction of data from required datasets. After completion of process, PAM engine materialize the extracted data for reuse. The technique works and it reduces the processing time and improves response time.
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