Big Data and Information Analytics (BDIA)

ISSN 2380-6966(print)
ISSN 2380-6974(online)

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Online First

Big Data and Information Analytics (BigDIA) is an interdisciplinary quarterly journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing.

The journal papers will be organized quarterly and published online first. At the end of each year, there will be a hardcopy volume, consisting of the four issues. Institutes subscribing to the journal have access to the electronic access and can purchase a hard copy. Hard copies can also be sold individually.

BigDIA publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).

Archived in Portico and CLOCKSS

TOP 10 Most Read Articles in BDIA, March 2017


A new training program in data analytics & visualization
Volume 1, Number 1,  Pages: i - iii,  2015

James H. Elder

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Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM)
Volume 1, Number 1,  Pages: v - v,  2016

Jimmy Huang, Ali Asgary and Jianhong Wu

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ACO-based solution for computation offloading in mobile cloud computing
Volume 1, Number 1,  Pages: 1 - 13,  2015

Weidong Bao, Haoran Ji, Xiaomin Zhu, Ji Wang, Wenhua Xiao and Jianhong Wu

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Towards big data processing in clouds: An online cost-minimization approach
Volume 1, Number 1,  Pages: 15 - 29,  2015

Weidong Bao, Wenhua Xiao, Haoran Ji, Chao Chen, Xiaomin Zhu and Jianhong Wu

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What's the big deal about big data?
Volume 1, Number 1,  Pages: 31 - 79,  2015

Nick Cercone, F'IEEE

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Spatio-temporal keywords queries in HBase
Volume 1, Number 1,  Pages: 81 - 91,  2015

Xiaoying Chen, Chong Zhang, Zonglin Shi and Weidong Xiao

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A soft subspace clustering algorithm with log-transformed distances
Volume 1, Number 1,  Pages: 93 - 109,  2015

Guojun Gan and Kun Chen

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Why curriculum learning & self-paced learning work in big/noisy data: A theoretical perspective
Volume 1, Number 1,  Pages: 111 - 127,  2015

Tieliang Gong, Qian Zhao, Deyu Meng and Zongben Xu

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On balancing between optimal and proportional categorical predictions
Volume 1, Number 1,  Pages: 129 - 137,  2015

Wenxue Huang and Yuanyi Pan

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