|3/2015 - 18|
HiGIS: An Open Framework for High Performance Geographic Information SystemXIONG, W. , CHEN, L.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,702 KB) | Citation | Downloads: 279 | Views: 1,828|
high performance computing, geographic information system, geocomputation, communicating sequential process
parallel(10), computing(8), cloud(7), system(6), data(6), processing(5), geospatial(5), remote(4), performance(4), high(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2015-08-31
Volume 15, Issue 3, Year 2015, On page(s): 123 - 132
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.03018
Web of Science Accession Number: 000360171500018
SCOPUS ID: 84940739658
/Big data/ era expose many challenges to geospatial data management, geocomputation and cartography. There is no exception in geographic information systems (GIS) community. Technologies and facilities of high performance computing (HPC) become more and more feasible to researchers, while mobile computing, ubiquitous computing, and cloud computing are emerging. But traditional GIS need to be improved to take advantages of all these evolutions. We proposed and implemented a GIS married with high performance computing, which is called HiGIS. The goal of HiGIS is to promote the performance of geocomputation by leveraging the power of HPC, and to build an open framework for geospatial data storing, processing, displaying and sharing. In this paper the architecture, data model and modules of the HiGIS system are introduced. A geocomputation scheduling engine based on communicating sequential process was designed to exploit spatial analysis and processing. Parallel I/O strategy using file view was proposed to improve the performance of geospatial raster data access. In order to support web-based online mapping, an interactive cartographic script was provided to represent a map. A demostration of locating house was used to manifest the characteristics of HiGIS. Parallel and concurrency performance experiments show the feasibility of this system.
|References|||||Cited By «-- Click to see who has cited this paper|
| A. G. Aly and N. M. Labib, "Proposed Model of GIS-based Cloud Computing Architecture for Emergency System," Int. J. Comput. Sci., vol. 1, no. 4, pp. 17-28, 2013.
 J. de la Torre, "Organising geo-temporal data with CartoDB. an open source database on the cloud," In Proc. Biodiversity Informatics Horizons, Rome, Italy, Sept. 2013
 S. Wang, "CyberGIS: blueprint for integrated and scalable geospatial software ecosystems," Int. J. Geogr. Inf. Sci., vol. 27, no. 11, pp. 2119-2121, 2013.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 18]
 I. H. Kim and M. H. Tsou, "Enabling Digital Earth simulation models using cloud computing or grid computing-two approaches supporting high-performance GIS simulation frameworks," Int. J. Digit. Earth, vol. 6, no. 4, pp. 383-403, 2013.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 19]
 A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz, "Hadoop gis: a high performance spatial data warehousing system over mapreduce," Proc. VLDB Endow., vol. 6, no. 11, pp. 1009-1020, 2013.
[CrossRef] [SCOPUS Times Cited 253]
 X. Guan, H. Wu, and L. Li, "A Parallel Framework for Processing Massive Spatial Data with a Split-and-Merge Paradigm," Trans. GIS, vol. 16, no. 6, pp. 829-843, 2012.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]
 W. Guo, X. Zhu, T. Hu, and L. Fan, "A Multi-granularity Parallel Model for Unified Remote Sensing Image Processing WebServices," Trans. GIS, vol. 16, no. 6, pp. 845-866, 2012.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]
 L. Liu, A. Yang, L. Chen, W. Xiong, Q. Wu, and N. Jing, "HiGIS - When GIS Meets HPC," In Proc. 12th Int. Conf. on GeoComputation, WuHan, 2013. [Online]. Available: http://www.geocomputation.org/2013/papers/26.pdf
 J. Liu, A.X. Zhu, Y. Liu, T. Zhu, and C.Z. Qin, "A layered approach to parallel computing for spatially distributed hydrological modeling," Environ. Model. Softw., vol. 51, no. 0, pp. 221 - 227, 2014.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 15]
 S. D. Brookes, C. A. R. Hoare, and A. W. Roscoe, "A Theory of Communicating Sequential Processes," J ACM, vol. 31, no. 3, pp. 560-599, Jun. 1984.
[CrossRef] [Web of Science Times Cited 523] [SCOPUS Times Cited 665]
 W. Guo, J.Y. Gong, W.S. Jiang, Y. Liu and G. She, "OpenRS-Cloud: A remote sensing image processing platform based on cloud computing environment," Sci. CHINA Technol. Sci., vol. 53, no. 1, pp. 221-230, 2010.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 30]
 Q. Chen, L. Wang, and Z. Shang, "MRGIS: A MapReduce-Enabled High Performance Workflow System for GIS," in Proc. of the 2008 Fourth IEEE Int. Conf. on eScience, Washington, DC, USA, 2008, pp. 646-651.
[CrossRef] [SCOPUS Times Cited 36]
 Y. Ma, D. Liu and J. Li, "A new framework of cluster-based parallel processing system for high-performance geo-computing," In Geoscience and Remote Sensing Symposium, Cape Town, 2009, vol. 4, pp. IV49-IV52.
[CrossRef] [SCOPUS Times Cited 2]
 T. Yuan, Y. Tang, X. Wu, Y. Zhang, H. Zhu, J. Guo, and W. Qin, "Formalization and Verification of REST on HTTP Using CSP," Electron. Notes Theor. Comput. Sci., vol. 309, no. 0, pp. 75-93, 2014.
[CrossRef] [SCOPUS Times Cited 3]
 G. Staples, "TORQUE Resource Manager," in Proc. of the 2006 ACM/IEEE Conf. on Supercomputing, New York, NY, USA, 2006.
[CrossRef] [SCOPUS Times Cited 73]
 D. Jackson, Q. Snell, and M. Clement, "Core Algorithms of the Maui Scheduler," in Job Scheduling Strategies for Parallel Processing, vol. 2221, D. Feitelson and L. Rudolph, Eds. Springer Berlin Heidelberg, 2001, pp. 87-102.
 S. Zhang, L. Chen, W. Xiong, "Research on performances of parallel programming models based on chip multi-processor," in Proc. 2011 Int. Conf. Computer Application and System Modeling, XiaMen, 2011, pp. 2688-2691.
 C. Yang, M. Goodchild, Q. Huang, D. Nebert, R. Raskin, Y. Xu, M. Bambacus, and D. Fay, "Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?," Int. J. Digit. Earth, vol. 4, no. 4, pp. 305-329, 2011.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 64]
 L. Ouyang, J. Huang, X. Wu, and B. Yu, "Parallel Access Optimization Technique for Geographic Raster Data," in Geo-Informatics in Resource Management and Sustainable Ecosystem, vol. 398, F. Bian, Y. Xie, X. Cui, and Y. Zeng, Eds. Springer Berlin Heidelberg, 2013, pp. 533-542.
[CrossRef] [SCOPUS Times Cited 2]
 C. Z. Qin, L. J. Zhan, and A. X. Zhu, "How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I/O?," Trans. GIS, vol. 18, no. 6, pp. 950-957, 2014.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 21]
 Y. Zou, W. Xue, and S. Liu, "A case study of large-scale parallel I/O analysis and optimization for numerical weather prediction system," Future Gener. Comput. Syst., vol. 37, no. 0, pp. 378-389, 2014.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 14]
 R. Thakur, W. Gropp, and E. Lusk, "Optimizing noncontiguous accesses in MPI-IO," Parallel Comput., vol. 28, no. 1, pp. 83 - 105, 2002.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 83]
 C. Heipke, "Crowdsourcing geospatial data," ISPRS J. Photogramm. Remote Sens., vol. 65, no. 6, pp. 550-557, 2010.
[CrossRef] [Web of Science Times Cited 172] [SCOPUS Times Cited 213]
Web of Science® Citations for all references: 908 TCR
SCOPUS® Citations for all references: 1,521 TCR
Web of Science® Average Citations per reference: 38 ACR
SCOPUS® Average Citations per reference: 63 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2018-12-12 07:35 in 140 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.