Fuzzy map approach for accruing velocity of big data
Year : 2019-04-01
Faculty : Information Technology
Author : وائل جمعة الزيادات / عايش منور هويشل الحروب / اخلاص المكحل /
Abstarct :
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.
Year : 2019-04-01
Faculty : Information Technology
Author : وائل جمعة الزيادات / عايش منور هويشل الحروب / اخلاص المكحل /
Abstarct :
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.