Author : Miss. Tejashri S. Nimbalkar 1
Date of Publication :7th July 2016
Abstract: Although the application of telemedicine evolutionary to cover a wide area of users’ needs, data centralization is not achieved yet. Also, approach to patient data from any remote location, through a secure environment, is necessary to achieve doctor’s collaboration and remote data handling. So web telemedicine database systems used for access to patient data from any location. The real world healthcare challenging application makes it hard to induce the database administrative staff. Traditional approaches for promote web telemedicine database systems focus on small networks involves minimum number of sites over the cloud. These are works on limited clustering algorithms. To overcome this Limitation, we need a new approach for promoting web telemedicine database system. In this project we proposed a novel Integrated Fragmentation clustering allocation approach for increase care admissions and decrease care difficulties on Approach that manages the computing web services that are required to promote telemedicine database system performance. Our approach focused on large scale networks involving large number of sites over the cloud. To perform more intelligent data redistribution, we apply different types of clustering algorithms and introduce search based techniques. The security concerns, need for addressing over data fragments will be taken into consideration for better results.
Reference :
-
- Planning High Performance Web-Based Computing Services to Promote Telemedicine Database Management System”IEEE TRANSACTIONS ON SERVICES COMPUTING,JANUARY 2015
- S. Lim and Y. Ng, ―Vertical Fragmentation and Allocation in Distributed Deductive Database Systems,‖ J. Information Systems, vol. 22, no. 1, pp. 1-24, 1997.
- S. Agrawal, V. Narasayya, and B. Yang, ―Integrating Vertical and Horizontal Partitioning into Automated Physical Database Design,‖ Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 359-370, 2004.
- S. Navathe, K. Karlapalem, and R. Minyoung, ―A Mixed Fragmen-tation Methodology for Initial Distributed Database Design,‖ J. Computer and Software Eng., vol. 3, no. 4, pp. 395-425, 1995
- W. Yee, M. Donahoo, and S. Navathe, ―A Framework for Server Data Fragment Grouping to Improve Server Scalability in Inter-mittently Synchronized Databases,‖ Proc. ACM Conf. Information and Knowledge Management (CIKM), 2000.
- A. Jain, M. Murty, and P. Flynn, ―Data Clustering: A Review,‖ ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
- Y. Huang and J. Chen, ―Fragment Allocation in Distributed Data-base Design,‖ J. Information Science and Eng., vol. 17, pp. 491-506, 2001.
- M. Halkidi, Y. Batistakis, and M. Vazirgiannis, ―Clustering Algo-rithms and Validity Measures,‖ Proc. 13th Int’l Conf. Scientific and Statistical Database Management (SSDBM), 2001.
- H. KhanS and L. Hoque, ―A New Technique for Database Frag-mentation in Distributed Systems,‖ Int’l J. Computer Applications, vol. 5, no. 9, pp. 20-24, 2010.
- G. Mao, M. Gao, and W. Yao, ―An Algorithm for Clustering XML Data Stream Using Sliding Window,‖ Proc. the Third Int’l Conf. Advances in Databases, Knowledge, and Data Applications, pp. 96-101, 2011.
- G. Decandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, ―Dynamo: Amazon‘s Highly Available Key-Value Store,‖ Proc. ACM Symp. Operating Systems Principles, pp. 205-220, 2007.