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Improved Skyline Query Retrieval using Particle Swarm Optimization Based Sweep Line Operator Over Real Time Datasets


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1 Department of Computer Science and Engineering, Thirumalai Engineering College, India
     

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In this paper initially clusters the search area’s slopes, i.e. it is shaped into settings according to its behavior in the search area, both past and present. In this study, these points were identified using a PSO control unit that works in a multi-dimensional search space. A PSO controller is employed to find the points in the search area under the new framework suggested in the paper. It contains several pre-processing methods for clearing incomplete or uncertain data in the area of unsafe data. Various preprocessing operations include: sort, fusion, filter and intercluster predominance in dimensional sets. The dominance between local points occurs at the nearest distance from each other. The points are of a global rank and sent in order to provide a rank according to the specific query to the PID controller. Moreover, the system proposed removes the point non-skyline in the search area by means of 2 new algorithms: Dynamic Pivot Sweep Line (DPSL), which can reduce the reaction time for a particular query. DPSL provides an ideal mechanism for searching the search area with skyline points, providing the best comparison. The DPSL algorithm is combined with real-life and synthetic preprocessing and PSO data sets for reducing storage and removal of redundant data in multi-dimensional search spaces. The whole controlled processing uses the values of the past and the present lines to predict future instances. This results in the dynamic query operation of the skyline and gets the whole data according to the specific query. In addition, PSO controller reduces response time, which saves more time than standard methods.

Keywords

In this paper initially clusters the search area’s slopes, i.e. it is shaped into settings according to its behavior in the search area, both past and present. In this study, these points were identified using a PSO control unit that works in a multi-dimensional search space. A PSO controller is employed to find the points in the search area under the new framework suggested in the paper. It contains several pre-processing methods for clearing incomplete or uncertain data in the area of unsafe data. Various preprocessing operations include: sort, fusion, filter and intercluster predominance in dimensional sets. The dominance between local points occurs at the nearest distance from each other. The points are of a global rank and sent in order to provide a rank according to the specific query to the PID controller. Moreover, the system proposed
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  • Improved Skyline Query Retrieval using Particle Swarm Optimization Based Sweep Line Operator Over Real Time Datasets

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Authors

A. Sairam
Department of Computer Science and Engineering, Thirumalai Engineering College, India
Seenuvasan Arumugam
Department of Computer Science and Engineering, Thirumalai Engineering College, India

Abstract


In this paper initially clusters the search area’s slopes, i.e. it is shaped into settings according to its behavior in the search area, both past and present. In this study, these points were identified using a PSO control unit that works in a multi-dimensional search space. A PSO controller is employed to find the points in the search area under the new framework suggested in the paper. It contains several pre-processing methods for clearing incomplete or uncertain data in the area of unsafe data. Various preprocessing operations include: sort, fusion, filter and intercluster predominance in dimensional sets. The dominance between local points occurs at the nearest distance from each other. The points are of a global rank and sent in order to provide a rank according to the specific query to the PID controller. Moreover, the system proposed removes the point non-skyline in the search area by means of 2 new algorithms: Dynamic Pivot Sweep Line (DPSL), which can reduce the reaction time for a particular query. DPSL provides an ideal mechanism for searching the search area with skyline points, providing the best comparison. The DPSL algorithm is combined with real-life and synthetic preprocessing and PSO data sets for reducing storage and removal of redundant data in multi-dimensional search spaces. The whole controlled processing uses the values of the past and the present lines to predict future instances. This results in the dynamic query operation of the skyline and gets the whole data according to the specific query. In addition, PSO controller reduces response time, which saves more time than standard methods.

Keywords


In this paper initially clusters the search area’s slopes, i.e. it is shaped into settings according to its behavior in the search area, both past and present. In this study, these points were identified using a PSO control unit that works in a multi-dimensional search space. A PSO controller is employed to find the points in the search area under the new framework suggested in the paper. It contains several pre-processing methods for clearing incomplete or uncertain data in the area of unsafe data. Various preprocessing operations include: sort, fusion, filter and intercluster predominance in dimensional sets. The dominance between local points occurs at the nearest distance from each other. The points are of a global rank and sent in order to provide a rank according to the specific query to the PID controller. Moreover, the system proposed

References