Deploying application over the web is increasing day by day. Such deployed application useful for client to store as well as retrieve database to/from particular server. Over the web data stored in distributed manner so flexibility, reliability, scalability and security are important aspects need to be considered while constructed data management system. After analyzing Distributed data aggregation service(DDAS) system which maintain a catalog which is relying on Blobseer it found that it provide a good performance in aspects such as data storage as a Blob (Binary large objects) and fast retrieval of data by data aggregation process. For highly complex analysis and instinctive mining of scientific data, Blobseer act as a repository backend for easy retrieval of data. By using this Kerberos method client will able to done a secure authentication as using this method only authorized clients are able to access distributed database. Kerberos consist of 4 steps i.e. Authentication Key exchange, Ticket granting service Key exchange, Client/Server service exchange and Build secure communication. After that aggregation of data carried out and aggregated data catalog is generated. From that catalog user is able to search a required data and this data in zip file form saved at client side. For zipping purpose Adaptive Huffman method is used (also referred to as Dynamic Huffman method) which is based on Huffman coding. It permits compression as well as decompression of aggregated data.

Adaptive Huffman Method; Blobseer; Distributed Database; Kerberos; Data Aggregation

[1] Suraj Gulhane, Sonali Bodkhe “DDAS using Kerberos with Adaptive Huffman Coding to enhance data retrieval speed and security” in Proceedings of the IEEE International Conference on International Conference on Pervasive Computing, IEEE Computer Society, 2015.
[2] Florin Pop, Gabriel Antoniu, Vlad Serbanescu, Valentin Cristea, “Architecture of Distributed Data Aggregation Service” in Proceedings of the 28th IEEE International Conference on Advanced Information Networking and Applications, IEEE Computer Society, 2014.
[3] K. Aamodt et al, “The ALICE experiment at the CERN LHC,” JINST, vol. 3, p.S08002,Augest 14,2008.
[4] S. Lanteri, J. Leduc, N. Melab, G. Mornet, R. Namyst, B. Quetier, O. Richard, F. Cappello, E. Caron, M. Dayde, F. Desprez, Y. Jegou, P. Primet, and E. Jeannot,“Grid’5000:Alarge scale and highly reconfigurable grid experimental testbed,” in Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing, ser. GRID’05.Washington,DC,USA: IEEE Computer Society, 2005.[Online].Available:http://dx.doi.org/10.1109/GRID.2005.1542730.
[5] X. Pennec,T. Glatard, and J. Montagnat,“ Efficient services composition for grid-enabled data-intensive applications,” in Proceedings of the IEEE International Symposium on High Performance and Distributed Computing,Jun.2006.[Online].Available:http://hal.archivesouvertes.fr/hal-00683201.
[6] S. Dustdar, W. Hummer, and P. Leitner, “Ws-aggregation: distributed aggregation of web services data,” in Proceedings of the 2011 ACM Symposium on Applied Computing, ser. SAC’11. New York, NY, USA: ACM, 2011.
[7] S. Leo power presentation on Python MapReduce Programming with Pydoop, “Pydoop: a python mapreduce and hdfs api for hadoop”.
[8] G. Antoniu, L. Boug´e, D. Moise, A. Carpen-Amarie, and B. Nicolae, “Blobseer: Next-generation data management for large scale infrastructures,” Author manuscript, published in “Journal of Parallel and DistributedComputing”71,2(2011).
[9] M. Ripeanu, S. Garfinkel, M. R. Palankar, and A. Iamnitchi, “Amazon s3 for science grids: a viable solution?” in Proceedings of the 2008 international workshop on Data-aware distributed computing, ser. DADC ’08. New York, NY, USA: ACM, 2008.
[10] K. Ramamohanarao, S. Venugopal, and R. Buyya, “A taxonomy of data grids for distributed data sharing, management, and processing,” ACM Computer Survey, vol.38, June 2006.
[11] M. Isard, Y. Yu, and P. K. Gunda, “Distributed aggregation for data parallel computing: interfaces and implementations,” in Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, ser. SOSP ’09.New York, NY, USA: ACM, 2009.
[12] Jaydip Sen “A Robust and Secure Aggregation Protocol for Wireless Sensor Networks” Innovation Lab, TCS Ltd.
[13] N. Chiwande, Prof. Animesh R. Tayal, and Ms. Vidya, “An Approach to Balance the Load with Security for Distributed File System in Cloud,” in Proceedings of 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies.
[14] Ranjita Bhagwan, Venkata N. Padmanabhan, Krishna P. N. Puttaswamy, “Anonymity-Preserving Data Aggregation using Anonygator,” Computer Science Department, UCSB, †Microsoft Research, India.
[15] Eunmi Choi, Subaji Mohan, Pilsung Kim, SangBum Kim, and Tran Doan Thanh, “A Taxonomy and Survey on Distributed File Systems” in Proceedings of Fourth International Conference on Networked Computing and Advanced Information Management,2008.
[16] Bogdan Nicolae, “BlobSeer: Efficient Data Management for Data Intensive Applications Distributed at Large-Scale,” University of Rennes IRISA, France.
[17] Gonzalo Navarro, Nieves Rodriguez Brisaboa, “New Compression Codes for Text Databases” University of Coruna (Espana).