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Efficient keyword query suggestion on document proximity using LKRS

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To set our current mechanism as scalable, we suggest a partition based approach that outplays the baseline algorithm by up to an offer the magnitude. The appropriateness of the current framework and the achievement of the algorithms are check out real locations data. | ISSN:2249-5789 Pippalla Lavanya et al, International Journal of Computer Science & Communication Networks,Vol 7(5),157-161 Efficient Keyword Query Suggestion on Document Proximity Using LKRS Pippalla Lavanya Ch.Vijaya Krishna M. Tech, Department of CSE, Shri Vishnu Engineering College for Women (A), Vishnupur, Bhimavaram, West Godavari District, Andhra Pradesh. M.Tech, Assistant professor, Department of CSE Shri Vishnu Engineering College for Women (A), Vishnupur, Bhimavaram, West Godavari District, Andhra Pradesh. Abstract Keyword and rank suggestion in web search support person to fetch similar spatial location without having to know how to exactly convey their queries. Pervious keyword suggestion mechanism do not taking the locations of the person and the similar query with the top scores as recommended. To set our current mechanism as scalable, we suggest a partition based approach that outplays the baseline algorithm by up to an offer the magnitude. The appropriateness of the current framework and the achievement of the algorithms are check out real locations data. results. The spatial relationship of a person to the similar results is not considered as a factor Keywords: in the suggestion. However, the similarity of baseline algorithm, partitioning, graph; proximity, LKRS, framework, location results in few applications is known to be similarity with their spatial relationship to INTRODUCTION the query presenter. In my current article, we Our current framework, we applied on design a location aware keyword rank query real world data. Here LTAS (Location aware (LKRS) suggestion framework. We suggest a type a head search) will finds documents nearer weighted keyword rank document graph, which to the user location. By using partitioning, gets both the similarity between keyword-rank baseline, Lq-ranking algorithms support we can queries and the spatial interval between the fetch good results. Baseline algorithm .