A scalable and productive workflow-based cloud platform for big data analytics

As data become big and complex, it is also more challenging to data scientists to extract useful information in a timely fashion. Although many tools and packages are available to them, it is crucial to have a high productive and scalable big data analytics platform to carry out their daily work pro...

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Bibliographic Details
Published in2016 IEEE International Conference on Big Data Analysis (ICBDA) pp. 1 - 5
Main Authors Chao Chen, Yuzhong Yan, Lei Huang, Xishuang Dong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2016
Subjects
Analytical models
Big data
Big Data Analytics
Cloud Computing
Data analysis
Data mining
Data models
Machine Learning
Predictive models
Sparks
Statistics
Workflow
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Summary:As data become big and complex, it is also more challenging to data scientists to extract useful information in a timely fashion. Although many tools and packages are available to them, it is crucial to have a high productive and scalable big data analytics platform to carry out their daily work productively. The objective for our work is to build such a productive data analytics cloud platform by integrating a variety of data analytics tools and packages with a high-level workflow interface. Data scientists are able to use the platform with the workflow interface to design their own pipelines in processing, analyzing, and visualizing data. In this paper, we present our design and implementation of the data analytics cloud architecture supporting a range from small data mining to big data analytics. We also demonstrate the usability of the platform using several case studies.
ISBN:1467395900
9781467395908
DOI:10.1109/ICBDA.2016.7509805