Progressive Optimization Research Articles

A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)

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).

Optimal Operation of Water Distribution Systems

Optimal operation of a water supply system is solved by progressive optimality (PO), an iterative dynamic programming (DP) method. Given the forecasted demands for the coming 24 hr, the initial and final conditions in the reservoirs, the hydraulic properties of all system components, and the variable energy cost over the day, an optimal schedule of pump operation is found. The algorithm cycles iteratively over the time steps (hours of the day) and network subsystems, and converges to the optimum from any (feasible or infeasible) initial solution. The global optimum is guaranteed only under certain conditions; otherwise a local optimum may be reached. The method is developed and demonstrated on a regional water supply system with eight reservoirs and seven pumping stations.