Abstract—Query Optimization is at the core for contribution
towards performance improvements in application systems. A
lot of ideas have been proposed towards Query Optimization
and there is lot of On-going research happening in this area.
Virtually every commercial query optimizer chooses the best
plan for a query using a cost model which is based on
cardinality estimation. If cardinality estimation is inaccurate,
then this may result in optimizer to choose a sub-optimal plan.
But once the optimizer chooses an optimal plan for execution
based on the approach of POP, the need for generating an
optimal plan for subsequent execution of the same query at a
later point in time can be minimized/reduced/exempted by
storing the execution plan. This paper proposes a Model for
building Dynamic Indexes & Storage and Re-Use of Optimal
Query plans generated thru Progressive Optimization (POP)
for performance gains. This approach is an extension to the
work implemented in “Robust Query Processing through
Progressive Optimization”. This paper proposes a model to
build Learning system within the database to analyze the
stream of incoming queries and project viable indexes as
against the initial indexes created by the Administrator and also
store and re-use of Optimal Query Plans generated thru
Progressive Query Optimization (POP).
Index Terms—QoS, PoP.
Sreekumar Vobugari and D. V. L. N. Somayajulu are with Department of Computer Science and Engineering at National Institute of Technology, Warangal, India (email: Sreekumar_vobugari@nitw.ac.in; firstname.lastname@example.org)
B. M. Subraya is with Education & Research Unit of Infosys Limited, Mysore, India (email:Subraya@gmail.com)
Cite:Sreekumar Vobugari, D. V. L. N. Somayajulu, and B. M. Subraya, "A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)," International Journal of Machine Learning and Computing vol.2, no. 4, pp. 471-475, 2012.