Applying computational geometry techniques to build data structure for range trees to assist the query for database

Abstract

The Fourth Industrial Revolution is the trend towards digital technologies to lead large databases and complicated database storages on Database Management System. At first sight it seems that databases have little to do with geometry. Nevertheless, queries about data in a database can be interpreted geometrically. To this end we transform records in a database into points in a multi-dimensional space, and we transform the queries about the records into queries on this set of points. This paper proposed an approach based on computational geometry techniques for building range trees, two-dimensional range search algorithm (search range trees algorithm) and general sets of points to improve the algorithm. To prove for proposed theorem, we had experimental research of Range-Tree structure on the different datasets. The experimental results of an orthogonal range query evaluated with the recently published method of KD-Tree on the same dataset. This proof shows that our proposed method is more time-efficient in queries