Query Optimization in NoSQL Databases Using an Enhanced Localized R-tree Index

Abstract

Query optimization is a crucial process across data mining and big data analytics. As the size of the data in the modern applications is increasing due to various sources, types and multi-modal records across databases, there is an urge to optimize lookup and search operations. Therefore, indexes can be utilized to solve the matter of rapid data growth as they enhance the performance of the database and subsequently the cloud server where it is stored. In this paper an index on spatial data, i.e. coordinates on the plane or on the map is presented. This index is be based on the R-Tree which is suitable for spatial data and is distributed so that it can scale and adapt to massive amounts of data without losing its performance. The results of the proposed method are encouraging across all experiments and future directions of this work include experiments on skewed data.