Nothing Special   »   [go: up one dir, main page]

Skip to main content

The Architecture of High-Performance GIS

  • Chapter
  • First Online:
High Performance Geographic Information System
  • 46 Accesses

Abstract

The focus of existing GIS is either on data management or on algorithm performance. There is a lack of comprehensive solutions for data management, geographic computing, and visualization. At the service level, existing systems emphasize the extensibility of services rather than high performance. The future development trend is to combine the latest computer technology and information technology, with a focus on solving problems related to large-scale geographic information applications. GIS should integrate with emerging application environments such as mobile internet, and enhance its integrated development with applications in related fields. Under the support of new computing architectures, new GIS software platforms need to construct a high-performance geospatial computation, spatial analysis, and storage environment at the infrastructure level, and build a wide-ranging and multi-tiered geospatial information service system at the upper level.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 199.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Zhang J, You S, Gruenwald L, et al. Large-scale Spatial Data Processing on GPUs and GPU-Accelerated Clusters [J]. Sigspatial Special, 2015, 6(3): 27–34.

    Article  Google Scholar 

  2. Shi X, Lai C, Huang M, et al. Geocomputation over the Emerging Heterogeneous Computing Infrastructure [J]. Transactions in Gis, 2014: 3–24.

    Google Scholar 

  3. Xiong W, Chen L. HiGIS: An Open Framework for High Performance Geographic Information System [J]. Advances in Electrical and Computer Engineering, 2015, 15(3): 123–132.

    Article  Google Scholar 

  4. Eldawy A, Mokbel M F. The Era of Big Spatial Data [C]. International Conference on Data Engineering, 2015: 1424–1427.

    Google Scholar 

  5. Guan Q, Shi X, Huang M, et al. A Hybrid Parallel Cellular Automata Model for Urban Growth Simulation over GPU/CPU Heterogeneous Architectures [J]. International Journal of Geographical Information Science, 2016, 30(3): 494–514.

    Article  Google Scholar 

  6. Foster I, Kesselman C. Globus: a Metacomputing Infrastructure Toolkit [C]. IEEE International Conference on High Performance Computing Data and Analytics, 1997, 11(2): 115–128.

    Google Scholar 

  7. Cinquini L, Crichton D J, Mattmann C A, et al. The Earth System Grid Federation: an open infrastructure for access to distributed geospatial data [J]. Future Generation Computer Systems, 2014: 400–417.

    Google Scholar 

  8. Sara S., Willie T., Samad M. E. S. Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities [J]. ISPRS Int. J. Geo-Inf. 2020, 9(4): 240

    Article  Google Scholar 

  9. Shekhar S, Gunturi V, Evans M R, et al. Spatial Big-data Challenges Intersecting Mobility and Cloud Computing [C]. Data Engineering for Wireless and Mobile Access, 2012: 1–6.

    Google Scholar 

  10. Xiaochuang Y., Guoqing L., Big Spatial Vector Data Management: a Review [J], Big Earth Data, 2018, 2(1): 108–129

    Article  Google Scholar 

  11. Sun C, Wang Z, Wang K, et al. Adaptive BPEL Service Compositions via Variability Management: a Methodology and Supporting Platform [J]. International Journal of Web Services Research, 2019, 16(1): 37–69.

    Article  Google Scholar 

  12. Oinn T, Greenwood M, Addis M, et al. Taverna: Lessons in Creating a Workflow Environment for the Life Sciences [J]. Concurrency and Computation: Practice and Experience, 2006, 18(10): 1067–1100.

    Article  Google Scholar 

  13. Taylor I, Shields M, Wang I, et al. Visual Grid Workflow in Triana [J]. Journal of Grid Computing, 2005, 3(3–4): 153–169.

    Article  Google Scholar 

  14. Ludäscher B, Altintas I, Berkley C, et al. Scientific Workflow Management and the Kepler System [J]. Concurrency and Computation: Practice and Experience, 2006, 18(10): 1039–1065.

    Article  Google Scholar 

  15. Deelman E, Vahi K, Rynge M, et al. Pegasus in the Cloud: Science Automation through Workflow Technologies [J]. IEEE Internet Computing, 2016, 20(1): 70–76.

    Article  Google Scholar 

  16. Kalayci S, Dasgupta G, Fong L, et al. Distributed and Adaptive Execution of Condor DAGMan Workflows [C]//SEKE. 2010: 587–590.

    Google Scholar 

  17. Reich M, Liefeld T, Gould J, et al. GenePattern 2.0 [J]. Nature genetics, 2006, 38(5): 500.

    Google Scholar 

  18. Maitrey S, Jha C K. MapReduce: Simplified Data Analysis of Big Data [J]. Procedia Computer Science, 2015, 57: 563–571.

    Article  Google Scholar 

  19. Liew C S, Atkinson M P, Galea M, et al. Scientific Workflows: Moving Across Paradigms [J]. ACM Computing Surveys (CSUR), 2017, 49(4): 66.

    Article  Google Scholar 

  20. Von Laszewski G, Hategan M, Kodeboyina D. Java CoG Kit Workflow [M]//Workflows for e-Science. Springer, London, 2007: 340–356.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Xiong .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 National Defense Industry Press

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xiong, W., Wu, Y., Ouyang, X., Jia, Qr., Chen, H., Chen, L. (2024). The Architecture of High-Performance GIS. In: High Performance Geographic Information System. Springer, Singapore. https://doi.org/10.1007/978-981-97-7170-7_2

Download citation

Publish with us

Policies and ethics