Query Processing for Distributed data using AVL Tree

Query processing over large datasets in an efficient manner is a challenge. It can be very time-consuming to perform range queries on vast data that has several features. This problem is often encountered in implementing databases. One solution to solve this problem is to maintain the data in the form of self-balancing binary trees that can return range query results in logarithmic time if the result set is considerably small compared to the data. In this paper an efficient data storage model using AVL trees is presented to solve this problem. The solution also presents its use over a distributed dataset using Hadoop framework. The paper also presents the modeling of the solution to MapReduce to perform range queries over distributed data.