A Narravite Cloud Storage Management Using Multi-level Semantic Bonding Technique
K. Thamizhchelvi1, Y. Kalpana2
1K. Thamizhchelvi, Research Scholar, Department of Information Technology, Vels Institute of Science and Technology & Advance Studies, Chennai (Tamil Nadu), India.
2Dr. Y. Kalpana, Professor, Department of Information Technology, Vels Institute of Science and Technology & Advance Studies, Chennai (Tamil Nadu), India.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 449-453 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10680982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1068.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The problem of cloud storage management has been well studied. The growing size and types of data increases the challenge in storage and retrieval. Number of approaches has been discussed for the problem of storage management. Text based clustering algorithms and Semantic based approaches are defined to improve the performance of storage management. However, the methods suffer to achieve higher performance in indexing and retrieval in terms of storage management. To solve this issue, an efficient semantic bonding measure based clustering and data management algorithm is presented. The method maintains ontology of various classes where each class has been mentioned in multiple levels. Each level of a class has specific properties and values. Using the semantic ontology, the method estimates MSB (Multi-level semantic Bonding) measure for different class of data. The same has been estimated for different level of semantic classes. Indexing of document class is performed based on MSB where the documents similarity has been measured using Topical Closure Measure (TCM). According to the value of TCM, the documents which are similar are identified and merge. The proposed algorithm improves the performance of document clustering and storage management in cloud environment.
Keywords: Cloud Data, High Dimensional Clustering, Semantic Clustering, Topical Measures, MSB.
Scope of the Article: Cloud Computing and Networking