Hierarchical Data Classification and Automatic Retrieval for Location Based Publish and Subscribe System
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 12
Abstract
Geospatial data is becoming massive which leads to effective data management by compressing, updating and querying the data fields of the multidimensional spatial and temporal data. The explosion of both the data volumes and dimensionality of these geospatial field data makes the storage, management, query and processing a daunting challenge to existing solutions. In this work, hierarchical tensor decomposition based on the split-and-merge paradigm is developed for continuously compression and appending of multidimensional geospatial field data. the goal is to propose a hierarchical data structure to reformulate and store the large volume of geospatial field data and to develop methods for data storage, query and computation support using this data structure. The Proposed work is achieved through a prototype implementation. The prototype has five components: 1) the design of a buffered hierarchical data structure and data decomposition strategies; 2) a proposal for a blocked data separation mechanism for splitting the huge tensors into small blocks according to the spatial-temporal reference; 3) a proposed algorithm that allows for data appending which is free of arithmetical operations and also computationally adaptive with continuous compression; 4) the development of a hierarchical structure-preserving and dimensional-independent data query which needs only to reform the row of the matrix in the leaf node; 5) the provision of computational operators such as tensor addition and linear operations, as well as a hierarchical structure-preserving computational framework.
Authors and Affiliations
Dr. K. Velmurugan, Miss. N. Meenatchi
Keywords
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