Scalable Data Analysis and Query Processing

Abstract

Scalable data is the demand of many of the emerging technologies. In order to target scalability, query processing plays an important role. The target is to achieve the maximum performance in terms of less execution time and more output. This could be achieved with any selected data and implementing advanced algorithms in any platform. This paper has worked in ArcMap in order to handle data of maps through different layers. Map reduction in size eases the process of query processing and generates the resultant records much faster. In addition, query category contributes to scalability as well. Classifying a compound query into a simple one adds positive impact on the results. Query payload and cost are controlled by maintaining execution time of the query and enhancing retuned records per command. This is helpful in analyzing different map layers for selected area of interest. Scalable map data is selected, analyzed with different map layers and results are obtained of processed queries that clearly indicates the successful achievement of scalability of the data through controlled process of query handling in smart and efficient way.