An Online-Offline Combined Big Data Mining Platform

An Online-Offline Combined Big Data Mining Platform

Deep learning

Big data

Data handling

Application programming interfaces (API)

E-learning

Data mining

Learning algorithms

Libraries

Machine learning libraries are integral to a big data mining platform. There are three limitations on adopting current machine learning libraries in such a platform. First, these algorithms are not implemented for handling both online and offline big data analysis. Second, libraries exist in a variety of frameworks using different programming languages, which make it difficult in integrating several algorithms. Third, most machine learning libraries provides APIs for programming only, thus not user-friendly for those do not have a sufficient understanding of algorithms and those lack of programming skills. In this paper, we implement a comprehensive machine learning library including common algorithms and deep learning algorithms. We integrate this library at a platform level that allows both online and offline data analysis using this library. We further design a user-friendly portal that enables quick and agile data analysis practices. All of these form an Online-Offline Combined Big Data Mining Platform (OOBDP). We present a demonstration of big oil data analysis using this platform. We observe the that OOBDP can easily accommodate industrial requirement for adaptable data mining process, with personalized usage scenarios, and easy to use experiences. 2017 IEEE.

1220-1225

2018-January

Zhang, Weishan

Lv, Hao

Xu, Liang

Liu, Yan

Liu, Xin

Lu, Qinghua

Li, Zhongwei

Zhou, Jiehan

15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017, November 6, 2017 - November 11, 2017

2017

conferencePaper

10.1109/DASC-PICom-DataCom-CyberSciTec.2017.195

http://dx.doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.195