Fast Nearest Neighbor Search with Keywords. Abstract Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions.

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Presentation transcript:

Fast Nearest Neighbor Search with Keywords

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. Currently, the best solution to such queries is based on the IF2-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR2-tree in query response time significantly, often by a factor of orders of magnitude.

Existing System Aspatial database manages multidimensional objects (such as points, rectangles, etc.), and provides fast access to those objects based on different selection criteria. The importance of spatial databases is reflected by the con¬venience of modeling entities of reality in a geometric man¬ner. For example, locations of restaurants, hotels, hospitals and so on are often represented as points in a map, while larger extents such as parks, lakes, and landscapes often as a combination of rectangles. Many functionalities of a spa¬tial database are useful in various ways in specific contexts. For instance, in a geography information system, range search can be deployed to find all restaurants in a certain area, while nearest neighbor retrieval can discover the res¬taurant closest to a given address.

Architecture Diagram

System Specification HARDWARE REQUIREMENTS Processor : Intel Pentium IV Ram : 512 MB Hard Disk : 80 GB HDD SOFTWARE REQUIREMENTS Operating System : Windows XP / Windows 7 FrontEnd : Java BackEnd : MySQL 5

C ONCLUSION We have seen plenty of applications calling for a search engine that is able to efficiently support novel forms of spatial queries that are integrated with keyword search. The existing solutions to such queries either incur pro¬hibitive space consumption or are unable to give real time answers. In this paper, we have remedied the situa¬tion by developing an access method called the spatial inverted index (SI-index). Not only that the SI- index is fairly space economical, but also it has the ability to per¬form keyword-augmented nearest neighbor search in time that is at the order of dozens of milli-seconds. Fur¬thermore, as the SI-index is based on the conventional technology of inverted index, it is readily incorporable in a commercial search engine that applies massive parallel¬ism, implying its immediate industrial merits.

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