Abstract: Conventional spatial queries, such as vary search and nearest neighbour retrieval, involve solely conditions on objects’ geometric properties. Today, several trendy applications call for novel varieties of queries that aim to seek out objects satisfying both a spatial predicate, and a predicate on their associated texts. As an example, rather than considering all the restaurants, a nearest neighbour query would instead invite the restaurant that is the nearest among those whose menus contain “steak, spaghetti, brandy” all at identical time. Presently the simplest solution to such queries relies on the IR2-tree, which, as shown in this paper, includes a few deficiencies that seriously impact its efficiency. impelled by this, we have a tendency to develop a brand new access methodology called the spatial inverted index that extends the standard inverted index to address multidimensional information, and comes with algorithms which will answer nearest neighbour queries with keywords in real time. As verified by experiments, the projected techniques outperform the IR2- tree in query reaction time significantly, typically by an element of orders of magnitude.
Keywords: Information Retrieval Tree, Keyword Search, Spatial Inverted Index.
Title: Fast Searching With Keywords Using Data Mining
Author: Chandrashekhar
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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