THE STUDAY A ORIENTED APPLICATION DATA ACCESS PATTERN ANALYSIS AND PREDICTION
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.1611Keywords:
Data Access Pattern, Per Sistent Data, Stochastic Modelling, Bayesian Inference, Importance Analysis, Markov Chains, Monte CarloAbstract [English]
An innovative approach for assessing and forecasting object-oriented application runtime behavior in relation to data access patterns across their domain objects is presented in this study. Three different stochastic model implementations are used for the study and forecasts. Markov Chains, Importance Analysis, and Bayesian Inference provide the foundation of the models. The solution handles all required modifications to the target applications under examination in an entirely automated manner, eliminating the need for developer participation. Implementing the TPC-W and oo7 benchmarks validates the findings. Monte Carlo simulations have been used to simulate the oo7 benchmark as a stochas-tic process. It can be shown that our method yields accurate findings in relation to associated with collecting are minimal, between 5% and 9%. A wide range of object-oriented applications may be implemented using the system because to its sufficient flexibility.
References
Adali, S., Candan, K. S., Papakonstantinou, Y., and Subrahmanian, V. S. Query caching and optimization in distributed mediator systems.SIG- MOD Rec., 25:137–146, June 1996 DOI: https://doi.org/10.1145/235968.233327
Aitchison, J. and Dunsmore, I.Statistical Prediction Analysis. Cambridge University Press, New York, NY, USA, 1975 DOI: https://doi.org/10.1017/CBO9780511569647
Berg, B.Markov Chain Monte Carlo Simulations and Their Statistical Analysis. World Scientific, 2004. DOI: https://doi.org/10.1142/5602
Bernstein, P. A., Pal, S., and Shutt, D. Context- based prefetch an optimization for implementing objects on relations. The VLDB Journal, 9:177– 189, December 2000. DOI: https://doi.org/10.1007/s007780000027
Box, G. and Tiao, G.Bayesian Inference in Sta tis- tical Analysis. Wiley, New York, NY, USA, 1992. DOI: https://doi.org/10.1002/9781118033197
Brin, S. and Page, L. The anatomy of a large-scale hypertextual web search engine.Comput. Netw. ISDN Syst., 30:107–117, April 1998. DOI: https://doi.org/10.1016/S0169-7552(98)00110-X
Carey, M., Dewitt, D., and Naughton, J. The oo7 benchmark. In Proceedings of the ACM SIG- MOD International Conference on Management of Data, pages 12–21, 1993. DOI: https://doi.org/10.1145/170035.170041
Dar, S., Franklin, M. J., Jónsson, B. T., Srivastava, D., and Tan, M. Semantic data caching and replacement. In Proceedings of the 22th Inter- national Conference on Very Large Data Bases, VLDB ’96, pages 330–341, San Francisco, CA, USA, 1996. Morgan Kaufmann Publishers Inc.
DoD, U. S. Procedures for performing a failure mode effects and criticality analysis. MIL-STD- 1629A, 1984.
Drapeau, S., Roncancio, C., and Guerrero, E. Generating association rules for prefetching. In Proceedings of the ICDCS Workshop of Knowledge Discovery and Data Mining in the World- Wide Web, pages F15–F22, Taipei, Taiwan, 2000
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