Background and Objective: Spatial queries frequently used in Hadoop for significant data process.
However, vast and massive size of spatial information makes it difficult to process the spatial inquiries
proficiently, so they utilized the Hadoop system for process the Big Data. Boolean Queries & Geometry Boolean
Spatial Data for Query Optimization using Hadoop System are used. In this paper, a lightweight and
adaptable spatial data index for big data have discussed, which have used to process in Hadoop frameworks.
Results demonstrate the proficiency and adequacy of spatial ordering system for various spatial inquiries.
Methods: In this section, the different type of approaches are used which helps to understand the procedure
to develop an efficient system by involving the methods like efficient and scalable method for processing
Top-k spatial Boolean Queries, Efficient query processing in Geographic web search engines. Geographic
search engine query processing combines text and spatial data processing technique & Top-k spatial preference
Queries. In this work, the implementation of all the methods is done for comparative analysis.
Results and Discussion: The execution of algorithm gives results which show the difference of performance
over different data types. Three different graphs are presented here based on the different data inputs
indexing and data types. Results show that when the number of rows to be executed increases the performance
of geohash decreases, while the crucial point for change in performance of execution is not visible
due to sudden hike in number of rows returned.
Conclusion: The query processing have discussed in geographic web search engines. In this work a general
framework for ranking search results based on a combination of textual and spatial criteria, and proposed
several algorithms for efficiently executing ranked queries on very large collections have discussed. The integrated
of proposed algorithms into an existing high-performance search engine query processor and works
on evaluating them on a large data set and realistic geographic queries. The results shows that in many cases
geographic query processing can be performed at about the same level of efficiency as text-only queries.