Towards GPU-Accelerated Web-GIS for Query-Driven Visual Exploration

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10181)

Abstract

Web-GIS has played an important role in supporting accesses, visualization and analysis of geospatial data over the Web for the past two decades. However, most of existing WebGIS software stacks are not able to exploit increasingly available parallel computing power and provide the desired high performance to support more complex applications on large-scale geospatial data. Built on top our past works on developing high-performance spatial query processing techniques on Graphics Processing Units (GPUs), we propose a novel yet practical framework on developing a GPU-accelerated Web-GIS environment to support Query-Driven Visual Explorations (QDVE) on Big Spatial Data. An application case on visually exploring global biodiversity data is presented to demonstrate the feasibility and the efficiency of the proposed framework and related techniques on both the frontend and backend of the prototype system.

Keywords

Web-GIS GPU QDVE Spatial join Biodiversity 

References

  1. 1.
    Cary, A., Sun, Z., Hristidis, V., Rishe, N.: Experiences on processing spatial data with MapReduce. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 302–319. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02279-1_24 CrossRefGoogle Scholar
  2. 2.
    Grochow, K., Howe, B., Stoermer, M., Barga, R., Lazowska, E.: Client + cloud: Evaluating seamless architectures for visual data analytics in the ocean sciences. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 114–131. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13818-8_10 CrossRefGoogle Scholar
  3. 3.
    Aji, A., Wang, F., et al.: HadoopGIS: A high performance spatial data warehousing system over MapReduce. Proc. VLDB Endow. 6(11), 1009–1020 (2013)CrossRefGoogle Scholar
  4. 4.
    Eldawy, A., Mokbel, M.F., Jonathan, C.: HadoopViz: A MapReduce framework for extensible visualization of big spatial data. In: Proceedings of the ICDE 2016, pp. 601–612 (2016)Google Scholar
  5. 5.
    Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach, 5th edn. Morgan Kaufmann, Burlington (2011)MATHGoogle Scholar
  6. 6.
    Kirk, D.B., Hwu, W.W.: Programming Massively Parallel Processors: A Hands-on Approach, 2nd edn. Morgan Kaufmann, Burlington (2012)Google Scholar
  7. 7.
    Kendall, W., Glatter, M., et al.: Terascale data organization for discovering multivariate climatic trends. In: SuperComputing 2009, pp. 1–12 (2009)Google Scholar
  8. 8.
    Healey, R., Dowers, S., et al.: Parallel Processing Algorithms for GIS. CRC, Boca Raton (1997)Google Scholar
  9. 9.
    Clematis, A., Mineter, M., Marciano, R.: High performance computing with geographical data. Parallel Comput. 29(10), 1275–1279 (2003)CrossRefGoogle Scholar
  10. 10.
    You, S., Zhang, J., Gruenwald, L.: Parallel spatial query processing on GPUs using R-trees. In: Proceedings of the BigSpatial@SIGSPATIAL, pp. 23–31 (2013)Google Scholar
  11. 11.
    Zhang, J., You, S., Gruenwald, L.: Large-scale spatial data processing on GPUs and GPU-accelerated clusters. ACM SIGSPATIAL Spec. 6(3), 27–34 (2014)CrossRefGoogle Scholar
  12. 12.
    Zhang, J., You, S., Gruenwald, L.: High-performance spatial query processing on big taxi trip data using GPGPUs. In: Proceedings of the IEEE International Congress on Big Data, pp. 72–79 (2014)Google Scholar
  13. 13.
    Zhang, J., You, S., Gruenwald, L.: Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs. Inf. Syst. 44, 134–154 (2014)CrossRefGoogle Scholar
  14. 14.
    Zhang, J., You, S., Gruenwald, L.: Efficient parallel zonal statistics on large-scale global biodiversity data on GPUs. In: Proceedings of the BigSpatial@SIGSPATIAL, pp. 35–44 (2015)Google Scholar
  15. 15.
    Zhang, J., You, S., Xia, Y.: Prototyping a web-based high-performance visual analytics platform for origin-destination data: A case study of NYC taxi trip records. In: Proceedings of the UrbanGIS@SIGSPATIAL, pp. 16–23 (2015)Google Scholar
  16. 16.
    You, S., Zhang, J., Gruenwald, L.: High-performance polyline intersection based spatial join on GPU-accelerated clusters. In: Proceedings of the BigSpatial@SIGSPATIAL, pp. 42–49 (2016)Google Scholar
  17. 17.
    Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Pearson, London (2003)Google Scholar
  18. 18.
    Jacox, E.H., Samet, H.: Spatial join techniques. ACM TODS 32(1), 7 (2007)CrossRefGoogle Scholar
  19. 19.
    Gaede, V., Gunther, O.: Multidimensional access methods. Comput. Surv. 30(2), 170–231 (1998)CrossRefGoogle Scholar
  20. 20.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Publishers Inc., Burlington (2005)MATHGoogle Scholar
  21. 21.
    Zhang, J., Gertz, M., Gruenwald, L.: Efficiently managing large-scale raster species distribution data in PostgreSQL. In: Proceedings of the ACM-GIS 2009, pp. 316–325 (2009)Google Scholar
  22. 22.
    Zhang, J.: A high-performance web-based information system for publishing large-scale species range maps in support of biodiversity studies. Ecol. Inform. 8, 68–77 (2012)CrossRefGoogle Scholar
  23. 23.
    Zhang, J., You, S.: Supporting web-based visual exploration of large-scale raster geospatial data using binned min-max quadtree. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 379–396. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13818-8_27 CrossRefGoogle Scholar
  24. 24.
    Hijmans, R.J., Cameron, S.E., et al.: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25(15), 1965–1978 (2005). http://www.worldclim.org/current CrossRefGoogle Scholar
  25. 25.
    Zhang, J., You, S., Gruenwald, L.: Parallel quadtree coding of large-scale raster geospatial data on GPGPUs. In: Proceedings ACM-GIS 2011, pp. 457–460 (2011)Google Scholar
  26. 26.
    Zhang, J., You, S., Gruenwald, L.: High-performance quadtree constructions on large-scale geospatial rasters using GPGPU parallel primitives. IJGIS 27(11), 2207–2226 (2013)Google Scholar
  27. 27.
    Zhang, J., You, S.: Dynamic tiled map services: Supporting query-based visualization of large-scale raster geospatial data. In: Proceedings of the COM.GEO 2010 (2010)Google Scholar
  28. 28.
    Aref, G.A., Ilyas, I.F.: SP-GiST: an extensible database index for supporting space partitioning trees. J. Intell. Inf. Syst. (JIIS) 17(2/3), 215–240 (2001)CrossRefMATHGoogle Scholar
  29. 29.
    Aji, A., Teodoro, G., Wang, F.: Haggis: turbocharge a MapReduce based spatial data warehousing system with GPU engine. In: Proceedings of the ACM BigSpatial@SIGSPATIAL, pp. 15–20 (2014)Google Scholar
  30. 30.
    Chavan, H., Alghamdi, R., Mokbel, M.F.: Towards a GPU accelerated spatial computing framework. In: Proceedings of the ICDE Workshops, pp. 135–142 (2016)Google Scholar
  31. 31.
    Zhang, J., Gruenwald, L.: Embedding and extending GIS for exploratory analysis of large-scale species distribution data. In: Proceedings of the ACM-GIS 2008, article no. 28 (2008)Google Scholar
  32. 32.
    Bisby, F.A.: The quiet revolution: Biodiversity informatics and the Internet. Science 289(5488), 2309–2312 (2000)CrossRefGoogle Scholar
  33. 33.
    Cox, C., Moore, P.: Biogeography: An Ecological and Evolutionary Approach, 7th edn. Wiley, New York (2005)Google Scholar
  34. 34.
    Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343 (1996)Google Scholar
  35. 35.
    Keim, D.A., Mansmann, F., et al.: Challenges in visual data analysis. In: Proceedings of the IEEE Conference on Information Visualization, pp. 9–16 (2006)Google Scholar
  36. 36.
    McCool, M., Robison, A.D., Reinders, J.: Structured Parallel Programming: Patterns for Efficient Computation. Morgan Kaufmann, Burlington (2012)Google Scholar
  37. 37.
    Merrill, D., Grimshaw, A.S.: High performance and scalable radix sorting: a case study of implementing dynamic parallelism for GPU computing. Parallel Process. Lett. 21(2), 245–272 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science, City CollegeCity University of New YorkNew YorkUSA
  2. 2.Department of Computer Science, Graduate CenterCity University of New YorkNew YorkUSA
  3. 3.Department of Computer ScienceThe University of OklahomaNormanUSA

Personalised recommendations