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Title: Study Of Scalable Data Mining Techniques For Big Data Analysis
Researcher: Osama Abdelhadi Fetooh Ghoneim
Guide(s): Doreswamy
Keywords: Engineering and Technology,Computer Science,Computer Science Software Engineering
University: Mangalore University
Completed Date: 
Abstract: Big data is a term for massive data sets having large, more varied and complex structure with newlinethe difficulties of storing, analyzing and visualizing for further processes or results. The process newlineof research into massive amounts of data to reveal hidden patterns and secret correlations named newlineas big data analytics. These useful informations for companies or organizations with the help newlineof gaining richer and deeper insights and getting an advantage over the competition. For this newlinereason, big data implementations need to be analyzed and executed as accurately as possible. newlineThe thesis Defines the scalability and its two types horizontal and vertical, gives a brief explanation newlineof different big data analysis tools like Hadoop, Spark, H2O, and Flink. As well as, it newlineGives a brief overview of data mining techniques which includes supervised and unsupervised newlinemethods as well as regression analysis. newlinePresentation of the MapReduce technique for analyzing IoT data has been introduced. The newlinethesis discussed the concept of cluster analysis. A full description of K-means clustering algorithm newlinebased on MapReduce programming model is given. The results conducted from this work newlineproved the efficiency of using MapReduce in big data analysis specially IoT data. newline
Pagination: xi, 121
Appears in Departments:Department of Computer Science

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01_title page.pdfAttached File17.48 kBAdobe PDFView/Open
02_priliminary pages.pdf394.77 kBAdobe PDFView/Open
03_chapter1.pdf427.99 kBAdobe PDFView/Open
04_chapter2.pdf238.01 kBAdobe PDFView/Open
05_chapter3.pdf76.66 kBAdobe PDFView/Open
06_chapter4.pdf855.06 kBAdobe PDFView/Open
07_chapter5.pdf295.63 kBAdobe PDFView/Open
08_chapter6.pdf379.31 kBAdobe PDFView/Open
09_chapter7.pdf652.7 kBAdobe PDFView/Open
10_chapter8.pdf116.62 kBAdobe PDFView/Open
11_bibliography.pdf132.44 kBAdobe PDFView/Open
12_abstract.pdf96.72 kBAdobe PDFView/Open
13_synopsis.pdf1.76 MBAdobe PDFView/Open

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