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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2015 - 18

 HIGHLY CITED PAPER 

HiGIS: An Open Framework for High Performance Geographic Information System

XIONG, W. See more information about XIONG, W. on SCOPUS See more information about XIONG, W. on IEEExplore See more information about XIONG, W. on Web of Science, CHEN, L. See more information about CHEN, L. on SCOPUS See more information about CHEN, L. on SCOPUS See more information about CHEN, L. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,702 KB) | Citation | Downloads: 439 | Views: 2,576

Author keywords
high performance computing, geographic information system, geocomputation, communicating sequential process

References keywords
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

Abstract
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Full text preview
/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

[1] 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.

[2] 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

[3] 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 18] [SCOPUS Times Cited 20]


[4] 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 18] [SCOPUS Times Cited 23]


[5] 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 362]


[6] 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 9] [SCOPUS Times Cited 10]


[7] 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 3] [SCOPUS Times Cited 3]


[8] 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

[9] 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 22] [SCOPUS Times Cited 29]


[10] 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 549] [SCOPUS Times Cited 716]


[11] 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 31] [SCOPUS Times Cited 40]


[12] 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 37]


[13] 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]


[14] 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 7]


[15] G. Staples, "TORQUE Resource Manager," in Proc. of the 2006 ACM/IEEE Conf. on Supercomputing, New York, NY, USA, 2006.
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[16] 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.
[CrossRef] [SCOPUS Times Cited 212]


[17] 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.

[18] 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 65] [SCOPUS Times Cited 80]


[19] 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 3]


[20] 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 27] [SCOPUS Times Cited 30]


[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 15] [SCOPUS Times Cited 19]


[22] 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 64] [SCOPUS Times Cited 86]


[23] C. Heipke, "Crowdsourcing geospatial data," ISPRS J. Photogramm. Remote Sens., vol. 65, no. 6, pp. 550-557, 2010.
[CrossRef] [Web of Science Times Cited 227] [SCOPUS Times Cited 282]




References Weight

Web of Science® Citations for all references: 1,048 TCR
SCOPUS® Citations for all references: 2,055 TCR

Web of Science® Average Citations per reference: 44 ACR
SCOPUS® Average Citations per reference: 86 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 2021-01-13 09:48 in 128 seconds.




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