Best Keyword Cover Search

Abstract

It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.

Aim

The main aim is to reduce the number of candidate keyword covers generated in a spatial database.

Scope

The scope of this project is to generate much scalable algorithm called keyword nearest neighbor expansion NNE reduce the number of candidate keyword covers generated.

Existing System

The existing works are to focus on retrieving individual objects by specifying a query consisting of a query location and a set of query keywords and to retrieve multiple objects which together cover all query keywords.

Disadvantages

When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated.

Proposed System

This project proposes to find a number of individual objects, each of which is close to a query location and the associated keywords (or called document) are very relevant to a set of query keywords and also it proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE).

Advantages

The baseline algorithm generates a large number of candidate keyword covers which leads to dramatic performance drop when more query keywords are given. The proposed keyword-NNE algorithm applies a different processing strategy, i.e., searching local best solution for each object in a certain query keyword. As a consequence, the number of candidate keyword covers generated is significantly reduced. The analysis reveals that the number of candidate keyword covers which need to be further processed in keyword-NNE algorithm is optimal and processing each keyword candidate cover typically generates much less new candidate keyword covers in keyword-NNE algorithm than in the baseline algorithm.

System Architecture

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·       Processor                    -   Pentium –III

·      Speed            -    1.1 Ghz

·      RAM             -    256 MB(min)

·      Hard Disk              -   20 GB

·      Floppy Drive         -    1.44 MB

·      Key Board             -    Standard Windows Keyboard

·      Mouse           -    Two or Three Button Mouse

·      Monitor                 -    SVGA

SOFTWARE REQUIREMENTS:-

·      Operating System          : Windows  7                                  

·      Front End                      : JSP AND SERVLET

·      Database                       : MYSQL

References

Xin Li, Jiaheng Lu, Xiaofang Zhou “BEST KEYWORD COVER SEARCH” Knowledge and Data Engineering, IEEE Transactions on  (Volume:27 ,  Issue: 1 ) May 2014