Skip to main content
SpringerLink
Log in
Book cover

Intelligent Computing - Proceedings of the Computing Conference

CompCom 2019: Intelligent Computing pp 132–144Cite as

Refinement and Trust Modeling of Spatio-Temporal Big Data

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 998))

Abstract

The conventional studies of spatio-temporal data models and their big data applications cannot reliably reflect the large volume, heterogeneity and dynamics of spatio-temporal big data. In this paper, the structure and function expression of spatio-temporal metadata is analyzed. With fused and normalized spatio-temporal reference and data structure, the constraint rules of spatio-temporal big data refinement are proposed. Using the domain specific modeling (DSM) and the data granulation algorithms, an object-oriented modeling language, the thrust modeling of spatio-temporal big data, and the aggregated status correlation of unified model data are established. This work utilizes the trust modeling theory and the spatio-temporal data processing methods and defines a case study that converts spatio-temporal data into dynamic complex big data. This research paves the way for the trust modeling and validation of spatio-temporal big data.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Pelekis, N., Theodoulidis, B., Kopanakis, I., et al.: Literature review of spatio-temporal database models. Knowl. Eng. Rev. 19(3), 235–274 (2004)

    Article  Google Scholar 

  2. Rocha, L.V.D., Edelweiss, N., Iochpe, C.: Geo Frame-T: a temporal conceptual framework for data modeling. In Proceedings of the 9th ACM International Symposium on Advances in Geographic Information Systems, pp. 124–129. Atlanta (2001)

    Google Scholar 

  3. Hunter, G.J., Williamson, I.P.: The development of a historical digital cadastral database. Int. J. Geogr. Inf. Syst. 4(2), 169–179 (1990)

    Article  Google Scholar 

  4. Langran, G., Chrisman, N.R.: A framework for temporal geographic information. Int. J. Geogr. Inf. Geovis. 25(3), 1–14 (1988)

    Google Scholar 

  5. Zhang, W., Li, A.N., Jin, H.A., et al.: An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data. Remote Sens. 5(10), 5346–5368 (2013)

    Article  Google Scholar 

  6. Bertone, A., Burghardt, D.: A survey visual analytics for the spatio-temporal exploration of microblogging content. J. Geovis. Spat. Anal. 1(1), 2 (2017)

    Article  Google Scholar 

  7. Zhang, L.: Simulation on C/A codes and analysis of GPS/pseudolite signals acquisition. Sci. China Ser. E Technol. Sci. 52(5), 1459–1462 (2009)

    Article  Google Scholar 

  8. Voisard, A., David, B.: A database perspective on geospatial data modeling. IEEE Trans. Knowl. Data Eng. 14(2), 226–243 (2002)

    Article  Google Scholar 

  9. Yao, J., Vasilakos, A.V., Pedrycz, W.: Granular computing: perspectives and challenges. IEEE Trans. Cybern. 43(6), 1977–1989 (2013)

    Article  Google Scholar 

  10. Oppenheim, A.V., Schafer, R.W.: Discrete-Time Signal Processing, 3rd edn. Pearson Education Limited, Essex (2009)

    MATH  Google Scholar 

  11. Galić, Z.: Spatio-Temporal Data Streams. Briefs in Computer Science. Springer, New York (2016). ISSN 2191-5768

    Chapter  Google Scholar 

  12. Liu, M., Zhu, J., Zhu, Q., et al.: Optimization of simulation and visualization analysis of dam-failure flood disaster for diverse computing system. Int. J. Geogr. Inf. Sci. 31(9), 1891–1906 (2017)

    Article  Google Scholar 

  13. Peuquet, D.J.: Making space for time: issue in space-time data representation. Geo Inf. 5(1), 11–32 (2001)

    MATH  Google Scholar 

  14. He, J., Hoare, C.A.R., Sanders, J.W.: Data refinement refined. In: ESOP, pp. 187–196 (1986)

    Google Scholar 

  15. Gartner, H., Bergmann, A., Schmidt, A.: Object-oriented modeling of data sources as a tool for the integration of heterogeneous geoscientific information. Comput. Geosci. 27, 975–985 (2001)

    Article  Google Scholar 

  16. Xu, Y., Zhan, H., Yu, J., Sun, L.: Knowledge Modeling Method Based on Domain Specific Modeling Meta-Module, 1005-2895(2012)02-0074-05

    Google Scholar 

  17. Kingston, J., Macintosh, A.: Knowledge management through multi-perspective modeling: representing and distributing organizational memory. Knowl.-Based Syst. 13(2), 121–131 (2000)

    Article  Google Scholar 

  18. Wiig, K.M.: Knowledge management: an introduction and perspective. J. Knowl. Manage. 1, 6–14 (1997)

    Article  Google Scholar 

  19. Sanchez, M.A., Castillo, O., Castro, J.R., Rodríguez-Díaz, A.: Fuzzy granular gravitational clustering algorithm. In: North American Fuzzy Information Processing Society, pp. 1–6 (2012)

    Google Scholar 

  20. Pedrycz, W., Vukovich, G.: Granular computing with shadowed sets. Int. J. Intell. Syst. 17(2), 173–197 (2001)

    Article  Google Scholar 

Download references

Acknowledgments

I would like to express my gratitude to National Key R&D Program of China (No. 2018YFE0101000) for the opportunity given to us by providing us grants to perform our research.

I would also like to thank Prof. Yunxuan Zhou and Prof. Jifeng He in East China Normal University, and all persons who have contributed to the success of our research, either directly or indirectly.

Author information

Authors and Affiliations

  1. MOE International Joint Lab of Trustworthy Software, East China Normal University, Shanghai, 200062, China

    Lei Zhang

  2. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China

    Lei Zhang

Authors
  1. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Zhang .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, L. (2019). Refinement and Trust Modeling of Spatio-Temporal Big Data. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 998. Springer, Cham. https://doi.org/10.1007/978-3-030-22868-2_10

Download citation

Publish with us

Policies and ethics

Search

Navigation