CFP last date
01 May 2024
Call for Paper
June Edition
CAE solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 01 May 2024

Submit your paper
Know more
The week's pick
Responsive image
An Improve Shannon Fano Data Compression Algorithm using Residue Number System

T.D. Lawal L.O. Olatunbosun K.A. Gbolagade
Random Articles
Reseach Article

Scaling Effectivity of Research Contributions in Distributed Data mining over Grid Infrastructures

by Shahina Parveen M., G. Narsimha
journal cover thumbnail

Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 8
Year of Publication: 2015
Authors: Shahina Parveen M., G. Narsimha
10.5120/cae2015652008

Shahina Parveen M., G. Narsimha . Scaling Effectivity of Research Contributions in Distributed Data mining over Grid Infrastructures. Communications on Applied Electronics. 3, 8 ( December 2015), 17-27. DOI=10.5120/cae2015652008

@article{ 10.5120/cae2015652008,
author = { Shahina Parveen M., G. Narsimha },
title = { Scaling Effectivity of Research Contributions in Distributed Data mining over Grid Infrastructures },
journal = { Communications on Applied Electronics },
issue_date = { December 2015 },
volume = { 3 },
number = { 8 },
month = { December },
year = { 2015 },
issn = { 2394-4714 },
pages = { 17-27 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume3/number8/486-2015652008/ },
doi = { 10.5120/cae2015652008 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:44:10.352308+05:30
%A Shahina Parveen M.
%A G. Narsimha
%T Scaling Effectivity of Research Contributions in Distributed Data mining over Grid Infrastructures
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 8
%P 17-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the increasing need of data availability and cloud-based services, distributed database management has already gained a maximum momentum in technological advancement. With the data stored in distributed manner, performing distributed data mining is encountering challenges especially when the data is real-time, non-static, highly heterogeneous, unstructured, etc. Usually, such forms of distributed data management are only effective if it is managed over grid infrastructure, which offers a suitable arena to the technology to provide better performance. However, research work considering distributed data mining over grid interface has not been nurtured to the best point in the research community as compared to conventional data mining approaches. This manuscript discusses the research trends and its effectiveness about the techniques and adoptability of the prior literature towards distributed data mining.

References
  1. T. Jain.2013. Technology Advancement in Wireless Communication. International Journal of Scientific & Technology Research, vol. 2, Issue. 8
  2. R.S. Sengall.2015. Research and Applications in Global Supercomputing. IGI Global, Computers, pp. 672
  3. M.J. Shaw., C. Subramaniam., G. W. Tan., and M. E. Welge.2001.Knowledge management and data mining for marketing. Decision support systems, vol. 31, no. 1, pp.127-137
  4. D.T. Larose.2014. Discovering knowledge in data: an introduction to data mining, John Wiley & Sons
  5. H.R. Rollinson.2014. Using geochemical data: evaluation.presentation, interpretation. Rutledge
  6. G. Shi., M. Mortazavi., J.Chen., and V.G.R. Kotha.2015.Method and apparatus for providing highly-scalable network storage for well-gridded objects. U.S. Patent, vol. 8, pp.996-803
  7. G. Weichhart., A. Molina., D.Chen., L.E. Whitman., and F. Vernadat.2015. Challenges and current developments for Sensing, Smart and Sustainable Enterprise Systems. Computers in Industry
  8. S. Venugopal., R. Buyya., and K. Ramamohanarao.2006. A taxonomy of data grids for distributed data sharing, management, and processing. ACM Computing Surveys (CSUR), vol. 38, no. 1
  9. D.B. Keator., J.S. Grethe., D. Marcus., B. Ozyurt., S. Gadde., S.Murphy., S. Pieper.2008. A national human neuroimaging collaboratory enabled by the Biomedical Informatics Research Network (BIRN). Information Technology in Biomedicine, IEEE Transactions, vol.12, no. 2, pp. 162-172
  10. R.J. Wilson. 2001. The European DataGrid Project.Institute de Fisica d’Altes Energies and Colorado State University, Barcelona, Spain and Fort Collins, Colorado, USA
  11. B-H. Park and H. Kargupta.2002. Distributed data mining: Algorithms, systems, and applications
  12. H. Xiao.2010. Towards parallel and distributed computing in large-scale data mining: A survey. Technical University of Munich, Tech. Rep
  13. B.B. Ahamed., and S. Hariharan.2011. A Survey On Distributed Data Mining Process Via Grid. International Journal of Database Theory and Application, vol. 4, no. 3, pp.77-90
  14. S.M. Thampi.2012. Survey on Distributed Data Mining in P2P Networks. arXiv preprint arXiv, pp.1205-3231
  15. V.Sawant, D. J. Sanghvi, K. Shah.2015. A Review of Distributed Data Mining using Agents. International Journal of Advanced Technology & Engineering Research (IJATER)
  16. S. G. Devi.2015.A Survey on Distributed Data Mining and Its Trends. Retrieved, 23rd Nov, 2015
  17. S. Masih., and S.Tanwani.2014. Data Mining Techniques in Parallel and Distributed Environment-A Comprehensive Survey.
  18. D.L. Srinivasulu., B. S. Kumar., and V. G. Akula.2015. A Survey on Research Problems in Distributed Data Mining. Retrieved, 23rd Nov, 2015
  19. M. Cannataro., D. Talia., and P.Trunfio.2002. Distributed data mining on the grid. Future Generation Computer Systems, vol. 18, no. 8, pp.1101-1112
  20. M. Cannataro., D.Talia., and P.Trunfio.2002. Design of distributed data mining applications on the knowledge grid. In Proceedings NSF Workshop on Next Generation Data Mining, pp. 191-195
  21. P. Luo., K. Lü., Z. Shi., and Q. He.2007. Distributed data mining in grid computing environments. Future Generation Computer Systems, vol. 23, no. 1, pp. 84-91
  22. K. Cardona., J. Secretan., M. Georgiopoulos., and G. Anagnostopoulos.2007. A grid based system for data mining using MapReduce. Technical Report TR, AMALTHEA
  23. L. Huo., Y. Fang., and H. Hu.2008. Dynamic service replica on distributed data mining grid. In Computer Science and Software Engineering, International Conference, vol. 3, pp. 390-393
  24. D. Talia.2009. Distributed data mining tasks and patterns as services. In Euro-Par Workshops-Parallel Processing, Springer Berlin Heidelberg, pp. 415-422
  25. D. Talia., P. Trunfio., and O. Verta.2008. The Weka4WS framework for distributed data mining in service-oriented Grids. Concurrency and Computation: Practice and Experience, vol. 20, no. 16, pp. 1933-1951
  26. F. Huang., Z. Li., and X. Sun.2008.A data mining model in knowledge grid”, In Wireless Communications, Networking and Mobile Computing, WiCOM'08. 4th International Conference, pp. 1-4
  27. M.P. Atkinson., J.I. V.Hemert., L. Han., A.Hume., and C.S. Liew.2009. A distributed architecture for data mining and integration. In Proceedings of the second international workshop on Data-aware distributed computing, pp. 11-20
  28. M. Lackovic., D. Talia., and P. Trunfio.2009. A Service-Oriented Framework for Executing Data Mining Workflows on Grids. In Grid and Pervasive Computing Conference,. GPC'09. Workshops, pp. 72-79
  29. M. Brescia., S. Cavuoti., R.D.Abrusco., O.Laurino., and G.Longo.2012. DAME: A Distributed Data Mining and Exploration Framework within the Virtual Observatory. In Remote Instrumentation for e-Science and Related Aspects, Springer, pp.267-284
  30. M.B.H. Hmida., and Y. Slimani.2010. Meta-learning in grid-based data mining systems. International journal of communication networks and distributed systems, vol. 5, no. 3, pp. 214-228
  31. M. Kantarcioglu., and R. Nix.2010. Incentive compatible distributed data mining. In Social Computing (SocialCom), IEEE Second International Conference, pp. 735-742
  32. T.J. Oyana.2010.A new-fangled FES-k-means clustering algorithm for disease discovery and visual analytics. EURASIP Journal on Bioinformatics and Systems Biology, vol. 746021, no. 1
  33. V.S. Rao., and S. Vidyavathi.2010. Distributed data mining and mining multi-agent data. (IJCSE) International Journal on Computer Science and Engineering, vol. 2, no. 04, pp. 1237-1244
  34. R. Tlili., and Y.Slimani.2011. Executing association rule mining algorithms under a Grid computing environment. In Proceedings of the Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging, pp. 53-61
  35. A. Prusiewicz, and M. Zieba.2011. The proposal of Service Oriented Data Mining System for solving real-life classification and regression problems. In Technological Innovation for Sustainability, Springer Berlin Heidelberg, pp. 83-90
  36. M.F. Santos., W.Mathew., and C.F.Portela.2011. Grid Data Mining for Outcome Prediction in Intensive Care Medicine. Enterprise Information Systems, pp. 244-253
  37. R. Mallik., N. Sarda., H. Kargupta., and S. Bandyopadhyay.2011. Distributed data mining for sustainable smart grids. Proc. of ACM SustKDD, vol. 11, pp.1-6
  38. T.N. Pandey., N. Panda., and P. K. Sahu. 2012. Improving performance of distributed data mining (DDM) with multi-agent system. IJCSI International Journal of Computer Science, issues. 9, no. 2
  39. R.B. Prajapati., and S. Menaria.2012. Multi Agent-Based Distributed Data Mining. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 1, no. 10, pp. 76.
  40. Y. Zhang., D.Sow., D.Turaga., and M. V.D. Schaar.2014. A fast online learning algorithm for distributed mining of big data. ACM SIGMETRICS Performance Evaluation, vol.41, no. 4, pp. 90-93
  41. K. Belbachir., and H. Belbachir.2013. Parallel Mining Association Rules in Calculation Grids
  42. P.Vishvapath., S.Ramachandram., A.Govardhan.2014. GWSVM Algorithm for a Grid System. International Journal of Computer Science and Information Technologies, vol. 5 (5), pp. 6871-6876
  43. A. Amini., H.Saboohi., T.Y.Wah., and T. Herawan. 2014. A fast density-based clustering algorithm for real-time internet of things stream. The Scientific World Journal
  44. H. Maaß., H.K. Cakmak., F. Bach., R. Mikut., A. Harrabi., W. Süß., W. Jakob., Kl-U.Stucky., U.G. Kühnapfel., and V. Hagenmeyer.2015. Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis. EURASIP Journal on Advances in Signal Processing, no. 1, pp. 1-21
  45. A.O. Ogunde., O. Folorunso., and A.S. Sodiya.2015. The Design of an Adaptive Incremental Association Rule Mining System. In Proceedings of the World Congress on Engineering, vol. 1
  46. M. Rebbah., M.E.A. Yemres., M. Khaldi., and M. Debakla. 2015. Hybrid Distribution for Association Rules Extraction on Grid Computing. Retrieved, 24th Nov, 2015
  47. T. Srinivasan., and B. Palanisamy.2015. Scalable Clustering of High Dimensional Data Technique using SPCM with ANT Colony Optimization Intelligence. Hindawi, the Scientific World Journal, pp. 5
  48. G. Zhou.2015. Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining. Journal of Electrical and Computer Engineering, vol. 50, pp. 357-378
Index Terms

Computer Science
Information Sciences

Keywords

Distributed Data Mining Grid Infrastructure Mining Cloud

Powered by PhDFocusTM