Conventional spatial queries such as vary search and nearest neighbor 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 neighbor 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 neighbor 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. Spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects geometric properties. A spatial database manages multidimensional objects (such as points, rectangles, etc.), and provides fast access to those objects based on different selection criteria. Now-a-days many applications call a new form of queries to find the objects that 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 the specified keywords all at the sametime.IR2-tree is used in the existing system for providing best solution for finding nearest neighbor. This method has few deficiencies. So we implement the new method called spatial inverted index to improve the space and query efficiency. And enhanced search is used to search the required objects based on the user priority level. Thus the proposed algorithm is scalable to find the required objects.
R-tree, UML, diagrams, brandy, index, R-tree