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Best Keyword Cover Search

ABSTRACT
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. 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.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- Existing works focus on retrieving individual objects by specifying a query consisting of a query location and a set of query keywords	PROPOSED CONCEPT: - The proposed keyword NNE algorithm applies a different processing strategy
EXISTING TECHNIQUE:- Baseline algorithm	PROPOSED TECHNIQUE:- Keyword NNE algorithm
TECHNIQUE DEFINITION:- 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.	TECHNIQUE DEFINITION:- Keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated Compared to the baseline algorithm, the number of candidate keyword covers generated in keyword-NNE algorithm is significantly reduced.
DRAWBACKS:- Low performance keyword cover in with the low score	ADVANTAGES:- High performance keyword cover in with the highest score