Improved continuous query plan with cluster weighted dominant querying in synthetic datasets

The arrival of large voluminous continuous
queries sets for a given query leads an insignificant insights.
The elimination of certain data tuples occurs in order to
balance the system load. The streaming query removes the
improper data tuples and uses proper data tuples in the form of
defined tables or sets. However, major drawback occurs due
to unbounded streaming and inadequate access to end data.
Due to such constraints, many stream processing methods
makes the processed data unavailable for any applications or
to the related queries of neighborhood branches. This paper
avoids such problems during the process of data tuples at
the generation of queries. The study uses a streaming model
that executes effective query plans in continuous data. The
streaming model aims reduce the communication cost and
improves the scalability of continuous aggregation queries. It
sub-divides the client query and executes it over data aggregators
within the incoherent limit. A weighted dominant query
algorithm is formulated to provide the top dominant value
in each sub-query clusters. This reduces the cost for computation
in synthetic databases. The experimental results
proved that the proposed model with weighted dominant
query algorithm effectively improves scalability by reducing
the computational cost.