Stream mining for solar physics: Applications and implications for big solar data

@article{Battams2014StreamMF,
  title={Stream mining for solar physics: Applications and implications for big solar data},
  author={Karl Battams},
  journal={2014 IEEE International Conference on Big Data (Big Data)},
  year={2014},
  pages={18-26}
}
  • Karl Battams
  • Published in
    IEEE International Conference…
    2014
  • Computer Science, Physics
  • Modern advances in space technology have enabled the capture and recording of unprecedented volumes of data. In the field of solar physics this is most readily apparent with the advent of the Solar Dynamics Observatory (SDO), which returns in excess of 1 terabyte of data daily. While we now have sufficient capability to capture, transmit and store this information, the solar physics community now faces the new challenge of analysis and mining of high-volume and potentially boundless data sets… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.
    SHOWING 1-2 OF 2 CITATIONS

    Big Data Reduction Methods: A Survey

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    The Statistics of Streaming Sparse Regression

    VIEW 1 EXCERPT
    CITES BACKGROUND

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 34 REFERENCES

    Dimensionality Reduction and Forecasting on Streams

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Mining high-speed data streams

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Data Streams - Models and Algorithms

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    A review on real time data stream classification and adapting to various concept drift scenarios

    VIEW 1 EXCERPT

    A Dynamic Load Balancing Mechanism for Data Stream Processing on DDS Systems

    • R. O. Vasconcelos, M. Endler
    • M.Sc Thesis, Departamento de Informatica, PUC-Rio-Pontifıcia Universidade Católica do Rio de Janeiro, Rio de Janeiro,
    • 2013
    VIEW 1 EXCERPT

    E-CVFDT: An improving CVFDT method for concept drift data stream

    VIEW 1 EXCERPT

    A hybrid decision tree training method using data streams

    VIEW 2 EXCERPTS

    Addressing Concept-Evolution in Concept-Drifting Data Streams

    VIEW 1 EXCERPT