Fuzzy MAP approach for accruing velocity of big data
Keywords:
Big Data, Velocity, Fuzzy Logic Controller, MapReduceAbstract
Each characteristic of Big Data (volume, velocity, variety, and value) illustrate a unique challenge to Big Data Analytics. The performance of Big Data from velocity characteristic, in particular, appear challenging of time complexity for reduced processing in dissimilar frameworks ranging from batch-oriented, MapReduce-based to real-time and stream-processing frameworks such as Spark and Storm. We proposed an approach to use a Fuzzy logic controller combined with MapReduce frameworks to handle the vehicle analysis by comparing the driving data from the new outcome vehicle trajectory. The proposed approach is evaluated via amount of raw data from the original resource with dataset after the processing of the approach using ANOVA to estimate and analyze the differences. The difference before and after using approach is a positive impact in several stages of the volume of datasets, variances, and P-value that mean significantly and contribute for two aspects i.e. accuracy and performance.
References
S. Ramírez-Gallego, A. Fernández, S. García, M. Chen, and F. Herrera, “Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce,” Inf. Fusion, vol. 42, pp. 51–61, 2018.
R. Kune, P. K. Konugurthi, A. Agarwal, R. R. Chillarige, and R. Buyya, “The anatomy of big data computing,” Softw.- Pract. Exp., vol. 46, no. 1, pp. 79–105, 2016.
Jin, Xiaolong, Benjamin W Wah, Xueqi Cheng, and Yuanzhuo Wang. 2015. 'Significance and challenges of big data research', Big Data Research, 2: 59-64.
Kaisler, S.H., Armour, F., Espinosa, J.A., & Money, W.H. (2013). Big Data: Issues and Challenges Moving Forward. 2013 46th Hawaii International Conference on System Sciences, 995-1004.
Fernández, Alberto, ara del Río, Abdullah Bawakid, and Francisco Herrera. 2017. 'Fuzzy rule based classification systems for big data with MapReduce: granularity analysis', Advances in Data Analysis and Classification, 11: 711-30
Faisal Y.Alzyoud and Wa‟el Jum‟ah Al_Zyadat., The classification filter techniques by field of application and thecresults of output. Aust. J. Basic & Appl. Sci., 10(15): 68-77, 2016
J. A. Benediktsson, Y. Zhu, M. Chi, Z. Sun, A. Plaza, and J. Shen, “Big Data for Remote Sensing: Challenges and Opportunities,” Proc. IEEE, vol. 104, no. 11, pp. 2207–2219, 2016.
S. Mahmud, R. Iqbal, and F. Doctor, “Cloud enabled data analytics and visualization framework for health-shocks prediction,” Futur. Gener. Comput. Syst., vol. 65, pp. 169–181, 2016.
Q. He, H. Wang, F. Zhuang, T. Shang, and Z. Shi, “Parallel sampling from big data with uncertainty distribution,” Fuzzy Sets Syst., vol. 258, pp. 117–133, 2015.
H. Wickham, J. Hester, R. Francois, J. Jylänki, and M. Jørgensen, “readr: read rectangular text data. R package version 1.1. 1.” R Foundation for Statistical Computing, 2017.
Wickham, H. and Francois, R. (2015) dplyr: A Grammar of Data Manipulation. R Package Version 0.4.3. http://CRAN.R-project.org/package=dplyr.
Wickham, H. (2017), tidyr: Easily Tidy Data with spread and gather Functions. R package version 0.6.1. URL: https://CRAN.R-roject.org/package=tidyr
M. Kuhn, “Classification and Regression Training (Caret),” R Program. Lang. Packag., 2015.
Rosenberg DS (2012). HadoopStreaming: Utilities for Using R Scripts in Hadoop Streaming. R package version 0.2, URL http://CRAN.R-project.org/package=HadoopStreaming.
J. Chung and M. B. A. Hanson, “Package „ HiveR ,‟” 2017.
T. R., Jon Garibaldi, Chao Chen, “Package „ FuzzyR ,‟”https://cran.r-project.org/web/packages/FuzzyR/FuzzyR.pdf.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
