A scientific data representation through particle flow based linear interpolation

Yu Pan, Feiyu Zhu, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Scientists often desire interactive visual analytics services to efficiently and effectively study their large-scale scientific data generated from simulations or observations. However, as the volume of scientific data is growing exponentially, it becomes increasingly difficult to achieve this goal for a typical interactive visual analytics system nowadays. The bottlenecks in visual analytics processes manifest in fetching time series data in a continuous manner. Since the changes in scientific datasets over a period of time are usually small and continuous, it is possible to learn an optical flow based representation of such dynamics. Therefore, the intermediate time steps of data can be efficiently inferred at run time. However, the existing optical flow determination methods cannot be directly applied to scientific datasets due to the highly complex non-rigid transformations in the feature space of scientific datasets. In this paper, we present a new method, named particle flow, that can capture the inherently complex dynamics of scientific datasets. We can effectively reconstruct any intermediate frames by interpolating the starting and ending frames using the resulting particle flow. We have also demonstrated that our approach can be effectively applied in data reduction for scientific datasets. Extensive experiments are conducted to show the accuracy and the efficiency of our approach over existing methods.

Original languageEnglish (US)
Title of host publicationProceedings - 5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019, Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering and Workshop on Medical, Healthcare, Using Big Data Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-28
Number of pages10
ISBN (Electronic)9781728100593
DOIs
StatePublished - Apr 2019
Event5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019 - Newark, United States
Duration: Apr 4 2019Apr 9 2019

Publication series

NameProceedings - 5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019, Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering and Workshop on Medical, Healthcare, Using Big Data Technologies

Conference

Conference5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019
CountryUnited States
CityNewark
Period4/4/194/9/19

Keywords

  • Data representation
  • Scientific data
  • Visualization

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Environmental Engineering
  • Water Science and Technology
  • Medicine (miscellaneous)
  • Health Informatics
  • Information Systems and Management
  • Health(social science)

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  • Cite this

    Pan, Y., Zhu, F., & Yu, H. (2019). A scientific data representation through particle flow based linear interpolation. In Proceedings - 5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019, Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering and Workshop on Medical, Healthcare, Using Big Data Technologies (pp. 19-28). [8848242] (Proceedings - 5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019, Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering and Workshop on Medical, Healthcare, Using Big Data Technologies). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataService.2019.00010