A Comparative Study of data structures for Proximity Search using Text Based Keywords
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Abstract
Conventional abstraction queries, like range search and nearest neighbour retrieval, involve only conditions on objects’ geometric properties. Today, several modern applications involve novel varieties of queries that aim to seek out objects satisfying each abstraction predicate, and a predicate on their associated texts. As an example, rather than considering all the restaurants, a nearest neighbour question would instead extract the eating house that's the highest among those whose menus contain “steak, spaghetti, brandy†all at an equivalent time. Presently the most suitable resolution to such queries is predicated on the IR2-tree, which, as shown during this paper, features a few deficiencies that seriously impact its potency. Motivated by this, a replacement access methodology has been developed which is known as the abstraction inverted index that extends the standard inverted index to address flat knowledge, and comes with algorithms that may answer nearest neighbour queries with keywords in real time. As scrutinised by experiments, the projected approaches outgo the IR2-tree in question latent period considerably, typically by an element of orders of magnitude.
Keywords: proximity search, keyword search, spatial index,IR2-tree,GPS.
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