DTNH Indexing Method: Past Present and Future Data Prediction for Spatio-Temporal Data

Abstract

Indexing methods are developed to effectively process user queries in many real-time and moving object management applications. The existing spatial data updating indexing methods are based on the Integrated binary Tree, R-Tree, R*-Tree, Oct-Tree, Quad-Tree, Grid-Tree and Hex-Tree. The depth of these trees is unbalanced and overlapping, hence the performance is reduced in the multi-structure indexing methods. D-Tree (Decompose -Tree) based multi-structure spatio-temporal index method is proposed to find the present, past and future data. The new multistructural model called DTNH-Tree used to find the present, past and future data. It consists of D-Tree, TB*-Tree, NTTree and hash table. The D-Tree indexing is used to get the spatial data and manage the moving objects in the road network. A set of TB*-Tree is used to index the history of moving object on road networks. A set of NT -Trees is used to manage the current position of the recently updated data and find present data of the moving objects. NT-Tree indexes the present and future information of the moving objects. Finally the set of hash tables is used for updating the data continuously. The proposed multi-structure indexing method supports different types of query processing compared to the existing indexing methods. Experimental results exhibits better updation and query performance compared to the MSMON-Tree and PPF*-Tree.