Probability Based String Transformation in Spatial Databases
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Abstract
Searching a string in large spatial database is a challenging task since it deals with geo-spatial capabilities. Specifically, the range queries are augmented with a string similarity search predicate in Euclidean space. Here, an probabilistic based string transformation techniques is proposed to perform efficient and effective query keyword search in IR2 index tree .The probabilistic based string transformation techniques embeds a log linear model which is used for learning based string transformation and top k candidate generation algorithm for generating optimal top –k candidate strings. It is possible to systematically illustrate combination of these techniques in Euclidean space and searching the index to find the most relevant answers.
Keywords: Approximate string search, probabilistic string transformation, spatial databases, IR2 tree, String Predicates
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