توضیحات
Abstract
In this study, we discuss the development of an analysis service that uses Hadoop for improving the performance of a database management system (DBMS)-based analysis service system that processes big data. DBMS-based systems are not suitable for processing big data and providing service because of their disadvantage in consuming more time for processing and analyzing. We introduced a distributed parallel platform, Hadoop ecosystem, for improving the performance of the system by minimizing the processing time in analyzing big data. In addition, we also carried
out a method of optimizing an existing analysis module to minimize the processing time. SPARK and Hive were implemented in the Hadoop platform to optimize the distributed parallel infrastructure. The developed analysis service system showed 127.87 times higher performance than that of other existing systems
Introduction
Nowadays, with increasing interest, big data analysis processing technology, which rapidly processes big data, has become a key technology in big data analysis. Big data is the term for a collection of data sets so large and complex
that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, creation, storage, search, sharing, transfer, analysis and visualization[1]. Hadoop is a distributed process technology that is widely used in current big data applications. Evolving to a high level, it can store many types of data and perform complex data analysis. Hadoop was created by Doug Cutting and Mike Cafarella in 2005. Apache Hadoop(High-Availability Distributed Object-Oriented Platform) is an open-source software framework for storage and large-scale processing of datasets on clusters of commodity hardware
Year:2015
By:Mikyoung Lee, Hanmin Jung, and Minhee Cho
File Information:English Language/8 Page/Size:368 K
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سال:2015
کاری از:Mikyoung Lee, Hanmin Jung, and Minhee Cho
اطلاعات فایل:زبان انگلیسی/8 صفحه/حجم:368 K
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