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Title: A paralleled big data algorithm with mapreduce framework for mining twitter data
Authors: Bing, L
Chan, KCC 
Keywords: Big data algorithm
Data mining
Social media
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, 2015, 7034776, p. 121-128 How to cite?
Abstract: Some recent studies have suggested that public opinions expressed in social media may be correlated with various social issues. To find out what actually can be discovered in social media data, we need data mining. Data mining approaches that can handle massive amount of data have recently been referred to as big data algorithms. In this paper, we propose a big data algorithm to handling Twitter data mining. Furthermore, to ensure scalability, MapReduce framework is adopted to parallelize the proposed algorithm. Through the experiments, the potential of the proposed algorithm can be demonstrated. Computationally, the speed of execution can be shown to increase significantly despite increases in data set size. In fact, the acceleration ratio increases as the size of the dataset increases, and as the number of Data Nodes increases.
Description: 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014, Sydney, 3-5 December 2014
ISBN: 9.78E+12
DOI: 10.1109/BDCloud.2014.26
Appears in Collections:Conference Paper

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