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MapReduce algorithm for weather dataset

Weather forecasting plays a vital role in human daily routine, business and their decisions. The technology for weather forecasting is evolving rapidly due to the critical needs in obtaining the accurate prediction results. From the literature exploration, the researchers have found that weather dat...

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Main Authors: Majid, Mazlina A., Romli, Awanis, Ahmad, Noraziah, Hammad, Khalid Adam Ismail
Format: Research Report
Language:English
Published: 2018
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/36383/1/Mapreduce%20algorithm%20for%20weather%20dataset.wm.pdf
http://umpir.ump.edu.my/id/eprint/36383/
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spelling my.ump.umpir.363832023-02-20T04:33:26Z http://umpir.ump.edu.my/id/eprint/36383/ MapReduce algorithm for weather dataset Majid, Mazlina A. Romli, Awanis Ahmad, Noraziah Hammad, Khalid Adam Ismail QA75 Electronic computers. Computer science QA76 Computer software Weather forecasting plays a vital role in human daily routine, business and their decisions. The technology for weather forecasting is evolving rapidly due to the critical needs in obtaining the accurate prediction results. From the literature exploration, the researchers have found that weather data is important to be analysed in form of structure data. Most of data in weather is represented in unstructured data with different attributes such as temperature, humidity, visibility, and pressure. These data were captured by different types of sensors. The weather data consists of high volumes, high velocity and variety of data which is reflects to the characteristics of Big Data. In addition, these characteristics also contribute to the complexity on the data processing and prediction. Big Data analytics is a new concept to process the Big Data. For weather data, this new concept will help to organise the data into structure data. The well-known method for Big Data analytics is MapReduce Model. Nevertheless, the usage of MapReduce Model in processing weather dataset is not widely explored. Therefore, this research is focus on analysing the weather dataset using MapReduce Algorithm. The historical dataset in 10 years’ period (1997 to 2007) has been used and this dataset is obtained from NOAA. This original dataset is stored in Hadoop Distributed File System. Next, MapReduce Algorithm is developed using Java programming. The algorithm is tested using small and big dataset. The temperature, humidity and visibility attributes from the dataset has been extracted by the MapReduce Algorithm into structure data. Graphical analysis has been used to represent the result from the MapReduce Algorithm. Results from the proposed algorithm have been compared with the existing model known as AWK (Alfred Aho, Peter Weinberger, and Brian Kernighan) model. The purpose of the comparison is to investigate the capability of the proposed model in parallel processing. The comparison results shown that MapReduce Algorithm has produced 37%, 25% and 11% less compared to AWK in term of processing time for 10GB, 5GB and 1GB data, respectively. This result has revealed the significant impact to the used of MapReduce Algorithm in weather prediction. In addition, the MapReduce results have discovered the significant pattern of temperature, humidity and visibility information which is valuable for the weather prediction. iv 2018 Research Report NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36383/1/Mapreduce%20algorithm%20for%20weather%20dataset.wm.pdf Majid, Mazlina A. and Romli, Awanis and Ahmad, Noraziah and Hammad, Khalid Adam Ismail (2018) MapReduce algorithm for weather dataset. , [Research Report: Research Report] (Unpublished)
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Majid, Mazlina A.
Romli, Awanis
Ahmad, Noraziah
Hammad, Khalid Adam Ismail
MapReduce algorithm for weather dataset
description Weather forecasting plays a vital role in human daily routine, business and their decisions. The technology for weather forecasting is evolving rapidly due to the critical needs in obtaining the accurate prediction results. From the literature exploration, the researchers have found that weather data is important to be analysed in form of structure data. Most of data in weather is represented in unstructured data with different attributes such as temperature, humidity, visibility, and pressure. These data were captured by different types of sensors. The weather data consists of high volumes, high velocity and variety of data which is reflects to the characteristics of Big Data. In addition, these characteristics also contribute to the complexity on the data processing and prediction. Big Data analytics is a new concept to process the Big Data. For weather data, this new concept will help to organise the data into structure data. The well-known method for Big Data analytics is MapReduce Model. Nevertheless, the usage of MapReduce Model in processing weather dataset is not widely explored. Therefore, this research is focus on analysing the weather dataset using MapReduce Algorithm. The historical dataset in 10 years’ period (1997 to 2007) has been used and this dataset is obtained from NOAA. This original dataset is stored in Hadoop Distributed File System. Next, MapReduce Algorithm is developed using Java programming. The algorithm is tested using small and big dataset. The temperature, humidity and visibility attributes from the dataset has been extracted by the MapReduce Algorithm into structure data. Graphical analysis has been used to represent the result from the MapReduce Algorithm. Results from the proposed algorithm have been compared with the existing model known as AWK (Alfred Aho, Peter Weinberger, and Brian Kernighan) model. The purpose of the comparison is to investigate the capability of the proposed model in parallel processing. The comparison results shown that MapReduce Algorithm has produced 37%, 25% and 11% less compared to AWK in term of processing time for 10GB, 5GB and 1GB data, respectively. This result has revealed the significant impact to the used of MapReduce Algorithm in weather prediction. In addition, the MapReduce results have discovered the significant pattern of temperature, humidity and visibility information which is valuable for the weather prediction. iv
format Research Report
author Majid, Mazlina A.
Romli, Awanis
Ahmad, Noraziah
Hammad, Khalid Adam Ismail
author_facet Majid, Mazlina A.
Romli, Awanis
Ahmad, Noraziah
Hammad, Khalid Adam Ismail
author_sort Majid, Mazlina A.
title MapReduce algorithm for weather dataset
title_short MapReduce algorithm for weather dataset
title_full MapReduce algorithm for weather dataset
title_fullStr MapReduce algorithm for weather dataset
title_full_unstemmed MapReduce algorithm for weather dataset
title_sort mapreduce algorithm for weather dataset
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/36383/1/Mapreduce%20algorithm%20for%20weather%20dataset.wm.pdf
http://umpir.ump.edu.my/id/eprint/36383/
_version_ 1758578245961252864
score 13.211869

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