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Reseach Article

Markov Chain Application in Object-Oriented Software Designing

by Santosh Kumar, Vipin Saxena
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 10
Year of Publication: 2013
Authors: Santosh Kumar, Vipin Saxena
10.5120/11878-7687

Santosh Kumar, Vipin Saxena . Markov Chain Application in Object-Oriented Software Designing. International Journal of Computer Applications. 69, 10 ( May 2013), 17-22. DOI=10.5120/11878-7687

@article{ 10.5120/11878-7687,
author = { Santosh Kumar, Vipin Saxena },
title = { Markov Chain Application in Object-Oriented Software Designing },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 10 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number10/11878-7687/ },
doi = { 10.5120/11878-7687 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:53.397621+05:30
%A Santosh Kumar
%A Vipin Saxena
%T Markov Chain Application in Object-Oriented Software Designing
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 10
%P 17-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the current scenario, the performance evaluation of the software system is one of the major factors of the software development that helps to develop the quality oriented software. There are many performances optimizing techniques which are used for evaluating the performance of the software systems. Many of the researchers have used the optimization techniques i. e. Markov chain to find out the performance of the object-oriented software design. The present papers is based upon the evaluating the performance of the designed UML model for a real case study of Life Insurance of India (LIC). The performance is evaluated for sharing the attributes by the UML classes. The concept of the probabilistic adjacency metric is used and Dijkstra's algorithm is applied to compute the optimal path.

References
  1. Boris M. and Chris C. 2009 Estimating the ratios of the stationary distribution values for Markov chains modeling evolutionary algorithms, Evolutionary Computation MIT Press Cambridge, MA, USA, Vol. 7, Issue 3, pp. 343-377.
  2. Sam G. and Dana R. 2009 Convergence rates of Markov chains for some self-assembly and non-saturated Ising models, Theoretical Computer Science Elsevier Science Publishers Ltd. Essex, UK, Vol. 410, Issue 15, pp 1417-1427.
  3. Daniel R. 2009 Explicit error bounds for lazy reversible Markov chain Monte Carlo, Journal of Complexity Academic Press, Inc. Orlando, FL, USA, Vil. 25, Issue 1, pp 11-24.
  4. Xiaofan L. , Liliang R. Fei Y. and Bang Y. 2009 Meteorological Drought Forecasting Using Markov Chain Model, International Conference on Environmental Science and Information Application Technology, ESIAT, 4-5 July, Vol. 2, pp. 23-26.
  5. Alzate M. A. 2009 Exact statistics of a complex Markov chain through state reduction: A satellite on-board switching example, IEEE Latin-American Conference on Communications, LATINCOM 10-11 Sept. , pp. 1-7.
  6. Jérôme L-L. and Wojciech P. 2010 Unsupervised segmentation of new semi-Markov chains hidden with long dependence noise, Signal Processing, Elsevier North-Holland, Inc. Amsterdam, The Netherlands, Vol. 90, Issue 11, pp. 2899-2910.
  7. Kota S. , Araki C. , Hashimukai S. Ogoshi Y. Mori M. and Taniguchi S. 2010 Kana-to-kanji conversion method using Markov chain model of words in bunsetsu, 4th International Universal Communication Symposium (IUCS), 18-19 Oct. 2010, pp 154 – 160.
  8. Guangyue H. and Marcus B. 2010 Entropy rate of continuous-state hidden Markov chains, IEEE International Symposium on Information Theory Proceedings (ISIT), 13-18 June, pp. 1468 – 1472.
  9. Kyomin J. , Shah D. and Jinwoo S. 2010 Distributed Averaging via Lifted Markov Chain, IEEE Transactions on Information Theory, Vol. 56, Issue 1, pp. 634-647.
  10. Alzate M. A. 2010 LatinCon07-Exact Statistics of a Complex Markov Chain through State Reduction: A Satellite On-board Switching Example, Latin America Transactions, IEEE (Revista IEEE America Latina), Vol. 8, Issue 4, pp. 403-409.
  11. Garci?a J. C. F. 2010 Interval type-2 Fuzzy Markov Chains: An approach, Fuzzy Information Processing Society (NAFIPS), Annual Meeting of the North American, 12-14 July 2010, pp. 1-6.
  12. Carpinone, A. , Langella R. , Testa A. and Giorgio M. 2010 Very short-term probabilistic wind power forecasting based on Markov chain models, IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 14-17 June, pp. 107-112.
  13. Adnan N. A. and Islam M. S. 2010 Correction and Interpolation of Noise Corrupted Voice Using Markov Chain Detection Technique, International Conference on Data Storage and Data Engineering (DSDE), 9-10 Feb, pp. 305-309.
  14. Mapp G. , Thakker D. and Gemikonakli O. 2010 Exploring a New Markov Chain Model for Multiqueue Systems, 12th International Conference on Computer Modeling and Simulation (UKSim), 24-26 March, pp. 592-597.
  15. Dirk S. 2011 Using Markov-chain mixing time estimates for the analysis of ant colony optimization, Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, ACM New York, NY, USA, pp 139-150.
  16. Weidong L. and Chuanrong Z. 2011 A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery, IEEE Transactions on Geosciences and Remote Sensing, Vol. 49, Issue 8, pp. 2983-2992.
  17. Nazin A. V. and Miller B. 2011 The mirror descent control algorithm for weakly regular homogeneous finite Markov chains with unknown mean losses, 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 12-15 Dec, pp. 1779-1783.
  18. Nikolay S. , Ionu F. , Alicia R. W. and Fabian M. 2012 Modelling of forest stand dynamics using Markov chains, Environmental Modeling & Software Elsevier Science Publishers B. V. Amsterdam, The Netherlands, Vol. 31, pp 64-75.
  19. Takehiko N. 2012 Markov chain analysis of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise, Computation Optimization and Applications Kluwer Academic Publishers Norwell, MA, USA, Vol. 52, Issue 2, pp 601-622.
  20. Chuhong F. , Ting L. , Lampropoulos G. A. and Anastassopoulos V. 2012 Markov Chain CFAR Detection for Polari metric Data Using Data Fusion, IEEE Transactions on, Geosciences and Remote Sensing, Vol. 50, Issue 2, pp 397-408.
  21. Sengupta D. , Maulik U. and Bandyopadhyay S. 2012 Weighted Markov Chain Based Aggregation of Biomolecule Orderings, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 9, Issue 3, pp. 924-933.
  22. Lopes V. V. Scholz T. Estanqueiro A. and Novais, A. Q. 2012 On the use of Markov chain models for the analysis of wind power time-series, 11th International Conference on Environment and Electrical Engineering (EEEIC), 18-25 May, pp. 770 – 775.
  23. Abdullatif and Pooley 2008 A Computer Assisted State Marking Methods For Extracting Performance Models From Design Models, International Journal of Simulation, Vol. 8, No. 3, pp 36-46.
Index Terms

Computer Science
Information Sciences

Keywords

UML Markov Chain Class Diagram Sequence Diagram Adjacency Metric and Dijkstra s Algorithm