Optimizined skyline queries over uncertain data using improved scalable framework

Skyline operator is an attractive tool for making decision while accessing skyline queries. Varied settings are allowed over this skyline operator, due to its easy use over skyline queries. Most skyline algorithms operates based on the principle of completeness, in mak- ing the value of data points to be known. However, in major case, the presence of incom- plete values in the dataset allows algorithms to behave ineffective over skyline queries. Conventional Algorithms failed in redefining the dominance notion and this incomplete data can be handled using proposed approach. To eliminate the incomplete data redun- dancy, a novel framework model is tested with two algorithms: Dynamic Pivot Sweep Line and Dynamic Pivot Reuse Algorithm. A proportional–integral–derivative (PID) controller framework is tested with realand synthetic datasets proved effective and efficient than conventional ones.