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Spatial Interpolation of Streaming Geosensor Network Data in the RISER System

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Book cover Web and Wireless Geographical Information Systems (W2GIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9080))

Abstract

Managing the data generated by emerging spatiotemporal data sources, such as geosensor networks, presents a growing challenge to traditional, offline GIS architectures. This paper explores the development of an end-to-end system for near real-time monitoring of environmental variables related to wildfire hazard, called RISER. The system is built upon a geosensor network and web-GIS technologies, connected by a stream-processing system. Aside from exploring the system architecture, this paper focuses specifically on the important role of stream processing as a bridge between data capture and web GIS, and as a spatial analysis engine. The paper highlights the compromise between efficiency and accuracy in spatiotemporal stream processing that must often be struck in the stream operator design. Using the specific example of spatial interpolation operators, the impact of changes to the configurations of spatial and temporal windows on the accuracy and efficiency of different spatial interpolation methods is evaluated.

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References

  1. Albers, S.: Online algorithms: A survey. Mathematical Programming 97(1–2), 3–26 (2003)

    MATH  MathSciNet  Google Scholar 

  2. Ali, M.H., Aref, W.G., Kamel, I.: Scalability management in sensor-network phenomenabases. In: 18th International Conference on Scientific and Statistical Database Management, 2006, pp. 91–100. IEEE (2006)

    Google Scholar 

  3. Ali, M.H., Mokbel, M.F., Aref, W.G.: Phenomenon-aware stream query processing. In: 2007 International Conference on Mobile Data Management, pp. 8–15. IEEE (2007)

    Google Scholar 

  4. Ali, M.H., Mokbel, M.F., Aref, W.G., Kamel, I.: Detection and tracking of discrete phenomena in sensor-network databases. In: SSDBM, pp. 163–172 (2005)

    Google Scholar 

  5. Armstrong, M.P., Marciano, R.J.: Local interpolation using a distributed parallel supercomputer. International Journal of Geographical Information Systems 10(6), 713–729 (1996)

    Article  Google Scholar 

  6. Cramer, B.E., Armstrong, M.P.: An evaluation of domain decomposition strategies for parallel spatial interpolation of surfaces. Geographical Analysis 31(2), 148–168 (1999)

    Article  Google Scholar 

  7. Ding, Y., Densham, P.J.: Spatial strategies for parallel spatial modelling. International Journal of Geographical Information Systems 10(6), 669–698 (1996)

    Article  Google Scholar 

  8. Duchon, J.: Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In: Constructive theory of functions of several variables, pp. 85–100. Springer (1977)

    Google Scholar 

  9. Guan, Q., Kyriakidis, P.C., Goodchild, M.F.: A parallel computing approach to fast geostatistical areal interpolation. International Journal of Geographical Information Science 25(8), 1241–1267 (2011)

    Article  Google Scholar 

  10. Hawick, K.A., Coddington, P.D., James, H.A.: Distributed frameworks and parallel algorithms for processing large-scale geographic data. Parallel Computing 29(10), 1297–1333 (2003)

    Article  Google Scholar 

  11. Huang, Q., Yang, C.: Optimizing grid computing configuration and scheduling for geospatial analysis: An example with interpolating DEM. Computers and Geosciences 37(2), 165–176 (2011)

    Article  Google Scholar 

  12. Jin, R., Goswami, A., Agrawal, G.: Fast and exact out-of-core and distributed \(k\)-means clustering. Knowledge and Information Systems 10(1), 17–40 (2006)

    Article  Google Scholar 

  13. Kamel, I., Al Aghbari, Z., Awad, T.: Mg-join: Detecting phenomena and their correlation in high dimensional data streams. Distributed and Parallel Databases 28(1), 67–92 (2010)

    Article  Google Scholar 

  14. Kidner, D.B., Rallings, P.J., Ware, J.A.: Parallel processing for terrain analysis in GIS: Visibility as a case study. GeoInformatica 1(2), 183–207 (1997)

    Article  Google Scholar 

  15. Nittel, S., Leung, K.T.: Parallelizing clustering of geoscientific data sets using data streams. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management, 2004, pp. 73–84. IEEE (2004)

    Google Scholar 

  16. Nittel, S., Leung, K.T., Braverman, A.: Scaling clustering algorithms for massive data sets using data streams. In: ICDE, vol. 4, p. 830 (2004)

    Google Scholar 

  17. Nittel, S., Whittier, J.C., Liang, Q.: Real-time spatial interpolation of continuous phenomena using mobile sensor data streams. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 530–533. ACM (2012)

    Google Scholar 

  18. Oliver, M.A., Webster, R.: Kriging: A method of interpolation for geographical information systems. International Journal of Geographical Information System 4(3), 313–332 (1990)

    Article  Google Scholar 

  19. Palpanas, T.: Real-time data analytics in sensor networks. In: Managing and Mining Sensor Data, pp. 173–210. Springer (2013)

    Google Scholar 

  20. Patroumpas, K., Sellis, T.: Semantics of spatially-aware windows over streaming moving objects. In: 2007 International Conference on Mobile Data Management, pp. 52–59. IEEE (2007)

    Google Scholar 

  21. Potamias, M., Patroumpas, K., Sellis, T.: Sampling trajectory streams with spatiotemporal criteria. In: 18th International Conference on Scientific and Statistical Database Management, 2006, pp. 275–284. IEEE (2006)

    Google Scholar 

  22. Tolhurst, K., Shields, B., Chong, D.: Phoenix: development and application of a bushfire risk management tool. Emergency Management Australia 23(4), 47–54 (2008)

    Google Scholar 

  23. Trajcevski, G., Cao, H., Scheuermanny, P., Wolfsonz, O., Vaccaro, D.: On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the 5th ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 19–26. ACM (2006)

    Google Scholar 

  24. Wang, S., Armstrong, M.P.: A quadtree approach to domain decomposition for spatial interpolation in grid computing environments. Parallel Computing 29(10), 1481–1504 (2003)

    Article  Google Scholar 

  25. Wolfson, O., Xu, B., Chamberlain, S., Jiang, L.: Moving objects databases: issues and solutions. In: Proc. Tenth International Conference on Scientific and Statistical Database Management (SSDM), pp. 111–122 (1998)

    Google Scholar 

  26. Worboys, M.: A unified model of spatial and temporal information. Computer Journal 8(4), 26–34 (1994)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Xu Zhong, Allison Kealy & Matt Duckham

  2. IBM Research Australia, Carlton, VIC, 3053, Australia

    Guy Sharon

Authors
  1. Xu Zhong
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  2. Allison Kealy
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  3. Guy Sharon
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  4. Matt Duckham
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Corresponding author

Correspondence to Xu Zhong .

Editor information

Editors and Affiliations

  1. Université Grenoble Alpes, Saint Martin d'Heres, France

    Jérôme Gensel

  2. University of Zurich - Irchel, Zurich, Switzerland

    Martin Tomko

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© 2015 Springer International Publishing Switzerland

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Zhong, X., Kealy, A., Sharon, G., Duckham, M. (2015). Spatial Interpolation of Streaming Geosensor Network Data in the RISER System. In: Gensel, J., Tomko, M. (eds) Web and Wireless Geographical Information Systems. W2GIS 2015. Lecture Notes in Computer Science(), vol 9080. Springer, Cham. https://doi.org/10.1007/978-3-319-18251-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-18251-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18250-6

  • Online ISBN: 978-3-319-18251-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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