Optimal top-K queries processing: Sampling and dynamic scheduling approach

An effective query processing plays an important role in the uncertain data streams. Specifically, multiple top-k queries processing on uncertain data streams obtained from large applications of several fields such as sensor network monitoring and internet traffic control requires periodic execution of queries and sharing results among them. The system that monitors uncertain events in such data streams manipulates the top-k queries. Here the problem is that systems were not designed to allow query results sharing which in turn leads to high computation cost and inaccurate response from the system. To overcome these issues (Queries results sharing), the system using a sampling algorithm for sample the top possible worlds from well-known possible worlds based on their high probability. System uses an optimal dynamic programming approach that split the multiple queries into number of groups. Then the query groups are scheduled and planned for sharing results to yield minimum computation cost. A faster greedy algorithm is used to reduce the time and storage space of the top-k queries based on the greedy rule. Thus the proposed approach allows sharing computation among multiple top-k queries and generates best plan of query execution.