Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Modeling Extraction Transformation Load Embedding Privacy Preservation using UML

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 6
Year of Publication: 2012
Authors:
Kiran P
S Sathish Kumar
Kavya N P
10.5120/7772-0854

Kiran P, Sathish S Kumar and Kavya N P. Article: Modeling Extraction Transformation Load Embedding Privacy Preservation using UML. International Journal of Computer Applications 50(6):1-5, July 2012. Full text available. BibTeX

@article{key:article,
	author = {Kiran P and S Sathish Kumar and Kavya N P},
	title = {Article: Modeling Extraction Transformation Load Embedding Privacy Preservation using UML},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {6},
	pages = {1-5},
	month = {July},
	note = {Full text available}
}

Abstract

Extraction Transformation Load plays an important phase in development of data warehouse due its complexity of selecting data from different location and having different structures. The recent industry of data warehouse is driven by Privacy Preserving Data Mining which ensures privacy of sensitive information during Mining and is a requirement of most Data Bases. Current approaches to modelling extraction transformation load do not include privacy representation in Conceptual Modelling. This paper proposes object-oriented approach to model Extraction Transformation Load embedding privacy preservation. The major components of extraction include Data Source, Source Identifier, Retrieval, Join, Privacy Preserving Area and Data Staging Area. All the above mentioned components have been modelled using Unified Modelling Language.

References

  • Fang Ying-lan and Han Bing, Design and Implementation of ETL Management Tool, In proc. of International Symposium on Knowledge Acquisition and Modeling, pp 446-449, 2009.
  • Xudong Song, Xiaolan Yan and Liguo Yang,Design ETL Metamodel based on UML Profile, In proc. of Second International Symposium on Knowledge Acquisition and Modeling, pp 69-72,2009.
  • Lunan Li, A Framework Study of ETL Processes Optimization Based on Metadata Repository, In proc of Computer Engineering and Technology (ICCET), pp 125-129, 2010.
  • Darshan M. Tank, Amit Ganatra and Y P Kosta, Speeding ETL Processing in Data Warehouses Using High-Performance Joins For Changed Data Capture (CDC), In proc. of International Conference on Advances in Recent Technologies in Communication and Computing, 2010
  • L. Muñoz, J. N. Mazón and J. Trujillo, ETL Process Modeling Conceptual for Data Warehouses: A Systematic Mapping Study, IEEE Latin America Transactions, vol. 9, no. 3, june 2011.
  • Alkis Simitsis, Panos Vassiliadis, and Timos Sellis, State-Space Optimization of ETL Workflows, IEEE Transactions On Knowledge and Data Engineering, vol. 17, no. 10, october 2005.
  • Alkis Simitsis, Chetan Gupta, Song Wang and Umeshwar Daya, Partitioning Real-Time ETL Workflows, In Proceedings of ICDE Workshops, pp 159-162, 2010.
  • M. Mrunalini, T. V. Suresh Kumar, D. Evangelin Geetha and K. Rajanikanth, Modelling of Data Extraction in ETL Processes Using UML 2. 0, DESIDOC Bulletin of Information Technology, Vol. 2, pp. 3-9, 2006.
  • M Mrunalini, T V Suresh Kumar and K Rajani Kanth, Simulating Secure Data Extraction in Extraction Transformation Loading (ETL) Processes, In proc. Of Third UKSim European Symposium on Computer Modeling and Simulation,2009.
  • G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu. Anonymizing tables. In Proc. of the 10th Int'l Conference on Database Theory, January 2005.
  • R. Bayardo and R. Agrawal. Data privacy through optimal k-anonymity. In Proc. of the 21st Int'l Conference on Data Engineering, April 2005.
  • K. Lefevre, D. J. Dewitt, R. Ramakrishna, Incognito: Efficient fulldomain k-anonymity, In Proc. of the 24th ACM SIGMOD International Conference on Managemant of Data, pp. 4960, Baltimore, Maryland, USA, 2005
  • P. Samarati and L. Sweeney. Generalizing data to pro¬vide anonymity when disclosing information. In Proc. of the 17th ACM SIGMOD-SIGACT-SIGART Sympo¬sium on the Principles of Database Systems, 188, 1998.