International Journal of Electrical, Electronics and Data Communication (IJEEDC)
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Apr. 2021
Submitted Papers : 80
Accepted Papers : 10
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Paper Published : 1483
No. of Authors : 4016
  Journal Paper


Paper Title :
Search For Given {Object Set} Based On Spatial Location From User Query

Author :Santosh I Halpati, D.M.Thakore

Article Citation :Santosh I Halpati ,D.M.Thakore , (2014 ) " Search For Given {Object Set} Based On Spatial Location From User Query " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 49-53, Volume-2,Issue-12

Abstract : With growing in searching capability many intelligent search engine application has been build that allow Spatial- Textual searching and also provide distance of nearest object from the query point. In order to retrieve the top k spatial- Textual searching result we study the two Spatial-Textual indexing method IR-Tree and ILQuad-Tree using inverted index and spatial index technique but seen they have issue regarding their performance and memory usage. In parallel study ILQuad-Tree shown good capability toward search engine mechanism but when it talk about searching through huge amount of geo-located data start which led to more memory usage and retrieval is not exact. We build a strategy to overcome with this challenge as it’s been huge Data and improve the performance for search engine by using learning technique and cluster based searching methodology. In this Paper we proposed a system having unsupervised learning concept for retrieve from different cluster and proposed system also doing distance based query search so using centroid method and inverted index. Latterly using K-Nearest neighboring for keyword searching that will give the exact and efficient result for the top k spatial- Textual keyword query search on region of road network occupied with object. To prove this, the experiment has been done on with the realistic data and clearly demonstrate the performance of method how efficient it is. Keywords: Spatial textual, Haversine, Cluster data, Centroid cluster method, K-nearest neighbor

Type : Research paper

Published : Volume-2,Issue-12


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