Volume : 2, Issue : 6, JUN 2016

BANKING ANALYSIS PORTAL

Rahul R. Nair, Rohan E. Mhetre, Avinash D. Zagade, Ganesh S. Thorat

Abstract

There are very less systems in the Application Market which connects the different banks to access the transactional data (i.e. credited and debited) of the user. A user may have many different bank accounts for which the user may find it difficult to keep a track of all the transactions he had performed from different accounts and so the user finds it difficult to organize his expenditures and earnings at the end of the month. The proposed system organizes all together expenditures and earnings of the user from various accounts. Distributed Data Aggregation Service (DDAS): as the name itself suggests that it is nothing but aggregation of data. In simple words, the data that is present at different locations are merged together in order to achieve greater speed, security and flexibility. There are several systems for database management and one of them is Distributed data aggregation service (DDAS) system which is relying on Blobseer. It is found that it provides a high level performance in aspects such as data storage as a Blob (Binary large objects) and data aggregation. For complicated analysis and instinctive mining of scientific data, Blobseer serve as a repository backend. In this paper  review the different aspects regarding Distributed data aggregation service (DDAS) and different approaches and case study regarding it and in which aspects it is useful over the globe. The main purpose of this system is to provide the security to user sensitive data. This can be achieved applying security to three different connections of the system namely, User-API, API-Bank, API-Database using different algorithms like DDAS, Hash Coding, etc..

Keywords

Distributed Data, Blobseer, BLOB (Binary Large Objects), Aggregation.

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References

1. S. Venugopal, R. Buyya, and K. Ramamohanarao, “A taxonomy of data grids for distributed
data sharing, management, and processing,” ACM Comput. Surv., vol. 38,
June 2006.
2. T. Glatard, J. Montagnat, and X. Pennec, “Efficient services composition for gridenabled
data-intensive applications,” in Proceedings of the IEEE International Symposium
on High Performance and Distributed Computing, Jun. 2006, pp. 333–334.
3. B. Nicolae, G. Antoniu, L. Boug´e, D. Moise, and A. Carpen-Amarie, “Blobseer: Nextgeneration
data management for large scale infrastructures,” J. Parallel Distrib.
Comput., vol. 71, pp. 169–184, February 2011.
4. 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.
5. G. Antoniu, L. Boug´e, D. Moise, A. Carpen-Amarie, and B. Nicolae, “Blobseer: Nextgeneration
data management for large scale infrastructures,” Author manuscript, published
in “Journal of Parallel and DistributedComputing”71,2(2011).
6. A. Brampton, A. MacQuire, I. A. Rai, N. J. P. Race, and L. Mathy, “Stealth distributed
hash table: a robust and flexible super-peered dht,” in Proceedings of the 2006 ACM
CoNEXT conference, ser. CoNEXT '06. New York, NY, USA: ACM, 2006, pp.
19:1–19:12.
7. F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra,
A. Fikes, and R. E. Gruber, “Bigtable: A distributed storage system for structured data,”
ACM Trans. Comput. Syst., vol. 26, pp. 4:1–4:26, June 2008.
8. M. R. Palankar, A. Iamnitchi, M. Ripeanu, and S. Garfinkel, “Amazon s3 for science
grids: a viable solution?” in Proceedings of the 2008 international workshop on Dataaware
distributed computing, ser. DADC '08. New York, NY, USA: ACM, 2008, pp.
55–64.
9. W. Hummer, P. Leitner, and S. Dustdar, “Ws-aggregation: distributed aggregation of
web services data,” in Proceedings of the 2011 ACM Symposium on Applied Comput-
58 International Educational Scientific Research Journal [IESRJ]
ing, ser. SAC '11. New York, NY, USA: ACM, 2011, pp. 1590–1597.
10. J. Chen, S. Sehrish, W.-K. Liao, A. Choudhary, and K. Schuchardt, “Improving the average
response time in collective i/o,” in Recent Advances in the Message Passing Interface,
ser. LNCS 6090, 2011, pp. 71–73.
11. Ashok Vemuri, Merlyn Mitra, Anand Bhushan. “Case Study: NextGen Client Data
Aggregation and Reporting.”
Resea