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Hybrid Clustering Framework for Multi-dimensional Array Data

Published:20 October 2015Publication History

ABSTRACT

As the satellite imagery containing multi-dimensional array data is currently used for analysis of various applications, the frameworks to analyze that sort of scientific data have been introduced.

To process the scientific data like the satellite imagery there are some restrictions: for the analysis of large-scale data the aggregated data would be stored in specified data formats, for the time-series analysis of the huge size the specified file system would be needed as the data is rapidly increased, and so on. Although Hadoop framework which is big data computing platform is popular to process the big data it is not feasible to handle the scientific data. It does not support to process the data in different scientific formats. On the other hand, though SciDB is the data management system to mainly process large-scale array data, it is not appropriate to analyze the scalable data of the time series. In this paper, we propose hybrid clustering framework, which is to process the scientific data composed of the multidimensional arrays with time series.

The proposed framework would address the issues to provide the framework both processing array-based scientific data and handling ever-increasing data at the same time.

References

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  • Published in

    cover image ACM Other conferences
    BigDAS '15: Proceedings of the 2015 International Conference on Big Data Applications and Services
    October 2015
    321 pages
    ISBN:9781450338462
    DOI:10.1145/2837060

    Copyright © 2015 ACM

    © 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 20 October 2015

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