Skip to main content
Log in

An integrated architectural framework for geoprocessing in cloud environment

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

Spatial data infrastructure had ensured the availability of geospatial data over the local and global boundaries to be used in more flexible and efficient manner. With the enhancement in web service technologies the need of geoprocessing the geospatial data from various heterogeneous sources over the web, to produce valuable information is of great interest among research community. Towards this implementation of geoprocessing web, open geospatial consortium (OGC) has provided several web processing service standards for handling the geoprocess over the web. Most of the current existing geoprocessing web platforms inherit the capabilities of these web processing service standards and protocols define by OGC. However there exist several challenges that includes, growing intensity of data, high performance computing, real time data processing, and to provide robust, reliable, and cost effective geoprocess services to the end user. The future of geo-processing lies in the adoption of cloud computing to overcome the existing challenges. Characteristics of cloud computing including scalability, elasticity, pay-per-use, and self provisioning, offers a more reliable, on demand, and cost effective geoprocessing services to its end user. In this context the study makes an attempt to analyze the current state of geoprocessing services over cloud, with exiting GIS Cloud platform solutions in the literature to counter the challenges of GIS cloud. Furthermore, study proposes a design and architectural framework for geoprocessing over Cloud platform, based on OGC standards. The architecture includes developing geoprocessing service, and data management services as an essential part while storing/processing the geospatial data from heterogeneous sources. The architecture model including data management and geoprocessing can be bind together in a cloud platform to have an on demand service as per the requirement of end user.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Ian, H. (2010). An introduction to geographical information systems. Bengaluru: Pearson Education India.

    Google Scholar 

  2. Pandey, S. (2010). Cloud computing technology and GIS applications. In 8th Asian symposium on geographic information systems from computer and engineering view (ASGIS 2010), ChongQing, China.

  3. Chappell, D. (2010). GIS in the cloud. In The ESRI example. http://www.esri.com/library/whitepapers/pdfs/gis-in-the-cloud-chappell.pdf. Accessed Jan 3 2017.

  4. Alfaqih, T. M., & Hassan, M. M. GIS Cloud: Integration between cloud things and geographic information systems (GIS) opportunities and challenges. International Journal on Computer Science and Engineering (IJCSE).

  5. GIS Lounge. (2016). http://www.gislounge.com/learn-aboutgis-in-the-cloud/. Accessed June 13 2016.

  6. Teaima, A., Hefny, H. A., & BahaaShabana, B. S. (2015). Enhancing performance of GIS on cloud computing. International Journal of Advanced Computer Science and Applications (IJACSA), 6(11), 44–48. doi:10.14569/IJACSA.2015.061106.

    Google Scholar 

  7. Feng, L., Apers, P. M., & Jonker, W. (2004). Towards context-aware data management for ambient intelligence. In International conference on database and expert systems applications (pp. 422–431). Springer.

  8. Karnatak, H. C., Saran, S., Bhatia, K., & Roy, P. S. (2007). Multicriteria spatial decision analysis in web GIS environment. Geoinformatica, 11(4), 407–429.

    Article  Google Scholar 

  9. Yang, J., & Wu, S. (2010). Studies on application of cloud computing techniques in GIS. In 2010 second IITA international conference on geoscience and remote sensing (IITA-GRS) (Vol. 1, pp. 492–495). IEEE.

  10. Diao, Z. J., & Guo, S. (2014). A research of GIS software application based on cloud computing. In Applied mechanics and materials (Vol. 513, pp. 2107–2110). Trans Tech Publ.

  11. Gao, P., Liu, Z., Xie, M., & Tian, K. (2015). The development of and prospects for private cloud GIS in China. Asian Journal of Geoinformatics, 14(4), 30–38.

    Google Scholar 

  12. Peng, Z.-R., & Tsou, M.-H. (2003). Internet GIS: Distributed geographic information services for the internet and wireless networks. Hoboken: Wiley.

    Google Scholar 

  13. Yang, C., Raskin, R., Goodchild, M., & Gahegan, M. (2010). Geospatial cyberinfrastructure: Past, present and future. Computers, Environment and Urban Systems, 34(4), 264–277.

    Article  Google Scholar 

  14. Baranski, B., Deelmann, T., & Schäffer, B. (2010). Pay-per-use revenue models for geoprocessing services in the cloud. In ISPRS proceedings of the 1st international workshop on pervasive web mapping, geoprocessing and services.

  15. Yang, C., Goodchild, M., Huang, Q., Nebert, D., Raskin, R., Xu, Y., et al. (2011). Spatial cloud computing: How can the geospatial sciences use and help shape cloud computing? International Journal of Digital Earth, 4(4), 305–329.

    Article  Google Scholar 

  16. Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2008). A break in the clouds: Towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1), 50–55.

    Article  Google Scholar 

  17. Yue, P., Gong, J., Di, L., Yuan, J., Sun, L., Sun, Z., et al. (2010). GeoPW: Laying blocks for the geospatial processing web. Transactions in GIS, 14(6), 755–772.

    Article  Google Scholar 

  18. Brauner, J., Foerster, T., Schaeffer, B., & Baranski, B. (2009). Towards a research agenda for geoprocessing services. In 12th AGILE international conference on geographic information science (Vol. 1, pp. 1–12). Hanover: IKG, Leibniz University of Hanover.

  19. Schäffer, B., Baranski, B., & Foerster, T. (2010). Towards spatial data infrastructures in the clouds. In Geospatial thinking (pp. 399–418). Springer.

  20. Blower, J. D. (2010). GIS in the cloud: Implementing a web map service on Google App Engine. In Proceedings of the 1st international conference and exhibition on computing for geospatial research and application (p. 34). ACM.

  21. Ludwig, B., & Coetzee, S. (2010). A comparison of platform as a service (PaaS) clouds with a detailed reference to security and geoprocessing services. In Proceedings of the 1st international workshop on pervasive web mapping, geoprocessing and services.

  22. Bhat, M. A., Shah, R. M., Ahmad, B., & Bhat, I. R. (2010). Cloud computing: A solution to Information Support Systems (ISS). International Journal of Computer Applications (0975–8887), 11(5), 6–9. https://pdfs.semanticscholar.org/707e/bde150b41b71dd2683756ed418321edd4e9f.pdf. Accessed Jan 3 2017.

  23. Bediroglu, S., Yildirim, V., & Erbas, S. (2014). Application of GIS analyzes with cloud computing. In FIG congress 2014 engaging the challengesEnhancing the relevance.

  24. Srinivas, J., Kumar, K. K., Babu, B. S., Chandra, N. S., & Babu, G. C. (2011). Geoportal—A spatial cloud information service. International Journal of Engineering Science and Technology, 3(11), 7930–7933.

    Google Scholar 

  25. Cui, D., Wu, Y., & Zhang, Q. (2010). Massive spatial data processing model based on cloud computing model. In 2010 third international joint conference on computational science and optimization (CSO) (Vol. 2, pp. 347–350). IEEE.

  26. Shao, Y., Di, L., Bai, Y., Guo, B., & Gong, J. (2012). Geoprocessing on the Amazon cloud computing platform—AWS. In 2012 first international conference on agro-geoinformatics (agro-geoinformatics) (pp. 1–6). IEEE.

  27. Aly, A. G., & Labib, N. M. (2013). Proposed model of gis-based cloud computing architecture for emergency system. International Journal Of Computer Science, 1(4), 17–28.

    Google Scholar 

  28. Mahmoud, E., Hegazy, O., & El-Dien, M. N. (2013). Integration of GIS and cloud computing for emergency system. International Journal Of Engineering and Computer Science, 2(10), 2889–2893.

    Google Scholar 

  29. Amin, R., Iqbal, M. M., Hussain, M., Iqbal, Z., & Saleem, N. (2016). A cloud based GIS application framework to analyze road accidents using windows azure. International Journal of Computer Science and Information Security, 14(1), 38–44.

    Google Scholar 

  30. Cheng, X., Gui, Z., Hu, K., Gao, S., Shen, P., & Wu, H. (2015). A cloud-based platform supporting geospatial collaboration for GIS education. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(6), 1–4.

    Google Scholar 

  31. Kouyoumjian, V. (2010). The new age of cloud computing and GIS. In ESRI white paper. http://www.esri.com/library/ebooks/gis-in-the-cloud.pdf. Accessed Jan 3 2017.

  32. Ayşan, A. İ., Yiğit, H., & Yilmaz, G. (2011). GIS applications in cloud computing platform and recent advances. In 2011 5th international conference on, recent advances in space technologies (RAST) (pp. 193–196). IEEE.

  33. Esri. (2016). GIS mapping software, solutions, services, map apps, and data. http://www.esri.com/. Accessed June 13 2016.

  34. GIS Cloud::It’s about the Apps, not the Maps!. (2016). http://www.giscloud.com. Accessed June 13 2016.

  35. OpenGeo Suite. Boundless. (2016). http://boundlessgeo.com/products/opengeo-suite/. Accessed July 16 2016.

  36. Evangelidis, K., Ntouros, K., Makridis, S., & Papatheodorou, C. (2014). Geospatial services in the cloud. Computers & Geosciences, 63, 116–122.

    Article  Google Scholar 

  37. Sahin, K., & Gumusay, M. (2008). Service oriented architecture (SOA) based web services for geographic information systems. In XXIst ISPRS Congress. Beijing (pp. 625–630). Citeseer.

  38. Zhou, L., Wang, R., Cui, C., & Xie, C. (2012). Gis application model based on cloud computing. In Network computing and information security (pp. 130–136). Springer. http://link.springer.com/chapter/10.1007/978-3-642-35211-9_17. Accessed Jan 3 2017.

  39. Azzam, T., & Robinson, D. (2013). GIS in evaluation utilizing the power of geographic information systems to represent evaluation data. American Journal of Evaluation, 34(2), 207–224.

    Article  Google Scholar 

  40. McKee, L., Reed, C., & Ramage, S. (2011). OGC standards and cloud computing (p. 14). Wayland, MA: Open Geospatial Consortium Inc.

    Google Scholar 

  41. Yue, P., Baumann, P., Bugbee, K., & Jiang, L. (2015). Towards intelligent GIServices. Earth Science Informatics, 8(3), 463–481.

    Article  Google Scholar 

  42. Karimi, H. A., Roongpiboonsopit, D., & Wang, H. (2011). Exploring real-time geoprocessing in cloud computing: Navigation services case study. Transactions in GIS, 15(5), 613–633.

    Article  Google Scholar 

  43. Yue, P., Zhou, H., Gong, J., & Hu, L. (2013). Geoprocessing in cloud computing platforms—A comparative analysis. International Journal of Digital Earth, 6(4), 404–425.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reem Adnan AL Kharouf.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AL Kharouf, R.A., Alzoubaidi, A.R. & Jweihan, M. An integrated architectural framework for geoprocessing in cloud environment. Spat. Inf. Res. 25, 89–97 (2017). https://doi.org/10.1007/s41324-016-0080-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-016-0080-4

Keywords

Navigation