A DYNAMIC INDEXING SCHEME FOR MULTIDIMENSIONAL DATA

We present a new dynamic index structure for multidimensional data. The considered index structure is based on an extended grid file concept. Strengths and weaknesses of the grid files were analyzed. Based on that analysis we proposed to strengthen the concept of grid files by considering their stripes as linear hash tables, introducing the concept of chunk and representing the grid file structure as a graph. As a result we significantly reduced the amount of disk operations. Efficient algorithms for storage and access of index directory are proposed, in order to minimize memory usage and lookup operations complexities. Estimations of complexities for these algorithms are presented. A comparison of our approach to support effective grid file structure with other known approaches is presented. This comparison shows effectiveness of suggested metadata storage environment. An estimation of directory size is presented. A prototype to support of our grid file concept has been created and experimentally compared with MongoDB (a renowned NoSQL database). Comparison results show effectiveness of our approach in the cases of given point lookup, lookup by wide ranges and closest objects lookup when considering more than one dimension, and also better memory usage