SURVEY ON NEAREST NEIGHBOUR SEARCH OVER SPATIAL KEYWORDS DATABASE

Abstract

Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on
objects geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects
satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the
restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus
contain “steak, spaghetti, brandy” all at the same time. A search engine is able to efficiently support novel forms of
spatial queries which are integrated with keyword explore. The accessible solutions to such queries also acquire
prohibitive space consumption or are unable to give real time answers. As today’s need is smart search from search
engine not just what they query but relevant to query and similar to that query and where they actually find that
product, place or person in real world. To provide such smart search results fast nearest neighbor search with keywords
technique by using spatial inverted index (SI-index) has been proposed by researchers. This technique has great
efficiency to provide results but processes huge data to fulfill queries. To overcome huge data pre-processing, proposed
algorithm reduces no of objects processed by this technique to minimize the memory and processing cost. Proposed
technique uses limits to fetch limited objects from the dataset or database.