A New Approach for Identifying Optimal Top - K Results Using Sorted and Random Access.

Ranking Queries will produce results based on some computed score. It is used to order the tuples based on their score. These queries involve joins where users are interested in top ?k join results. For joining the rank of different attributes, the pipeline hash rank join algorithm is used to minimize the cost of accessing the tuples and to reduce the CPU cost. At first the attributes that are to be joined are identified and then the local ranking is made based on their individual scores. So that the ranked result of different attributes can be joined. While joining, it is not necessary to access all the tuples because the users will not expect the exact answers. Instead users will search for the approximate results that are similar to their query. So, for each query the joining will be made based on user preferences. So in this paper we have introduced a pipeline hash rank join algorithm to reduce the CPU time by accessing few numbers of tuples.