KSR-Quadtree: an Intelligent Knowledge Storage and Retrieval Using Quadtree Structure

Abstract

The size of the data in modern applications grows at an increasing rate. The voluminous database stored in direct access storage device requires more number of disk accesses and the database requires rescanning due to frequent updates. This leads to increased retrieval time. Also, the information retrieved from repository of data cannot guide decision-makers or management to make correct decisions as humans. In this paper, we propose a Knowledge Based System which converts the existing database into Quad tree. Here, the quad trees are used in arriving at decisions on what kind of plants can be that grown in soil based on the domain information (edaphology-soil characteristics) given by the user. The domain information that is stored as an application database is converted into a knowledge base using the dynamic quad-tree data structure. This system is designed with two phases namely, (1) Knowledge storage (as quad-tree) and (2) Effective knowledge retrieval. In the first phase, the records from the database are converted into 2Dpoints and store the point recursively in a two-dimensional space with four recursive quadrants. In the second phase, knowledge retrieval is done with the help of the constructed knowledge base (XML architecture). This system transforms the voluminous database storage into a file thereby reducing the number of disk access and retrieval time. The rescanning of Database is also avoided and supports the edaphologists in making right decisions.