000			03537ntm a2200277 a 4500
001			vtls000101358
003			KUKTEM
005			20251117113350.0
008			170928t2017 my a f am 000 0 eng d
020			_aTHE0001157(Local)
039		9	_a201905141046 _bnazirah _c201710030928 _dfateeha _c201709281127 _dfateeha _y201709281110 _zfateeha
040			_aUMP
090			_aFSKKP .K435 2017 r Thesis
100	0		_aKhalid Adam Ismail Hammad
245	1	0	_aMapreduce algorithm for weather dataset / _cKhalid Adam Ismail Hammad
260			_aKuantan, Pahang : _bUMP, _c2017
300			_ax, 118 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM
500			_aFaculty of Computer Systems & Software Engineering
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2017
504			_aBibliography : p. 74-79
520	3		_aWeather 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.
610	2	0	_aFaculty of Computer Systems & Software Engineering _xDissertations
650		0	_aUniversities and Colleges _xDissertations
650		0	_aTheses
999			_aVIRTUA40 _c7424 _d7430
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5000*5020*5040*5200*6100*6500*6501*9992