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Xuan Shi
Department of Geosciences, University of Arkansas, Fayetteville, AR, U.S.A.

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Research articles
Published: 03 April 2018 in Big Earth Data
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High spectral, spatial, vertical and temporal resolution data are increasingly available and result in the serious challenge to process big remote-sensing images effectively and efficiently. This article introduced how to conduct supervised image classification by implementing maximum likelihood classification (MLC) over big image data on a field programmable gate array (FPGA) cloud. By comparing our prior work of implementing MLC on conventional cluster of multicore computers and graphics processing unit, it can be concluded that FPGAs can achieve the best performance in comparison to conventional CPU cluster and K40 GPU, and are more energy efficient. The proposed pipelined thread approach can be extended to other image-processing solutions to handle big data in the future.

ACS Style

Sen Ma; Xuan Shi; David Andrews. Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster. Big Earth Data 2018, 2, 144 -158.

AMA Style

Sen Ma, Xuan Shi, David Andrews. Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster. Big Earth Data. 2018; 2 (2):144-158.

Chicago/Turabian Style

Sen Ma; Xuan Shi; David Andrews. 2018. "Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster." Big Earth Data 2, no. 2: 144-158.

Research articles
Published: 02 January 2018 in Big Earth Data
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Digital earth science data originated from sensors aboard satellites and platforms such as airplane, UAV, and mobile systems are increasingly available with high spectral, spatial, vertical, and temporal resolution data. When such big earth science data are processed and analyzed via geocomputation solutions, or utilized in geospatial simulation or modeling, considerable computing power and resources are necessary to complete the tasks. While classic computer clusters equipped by central processing units (CPUs) and the new computing resources of graphics processing units (GPUs) have been deployed in handling big earth data, coprocessors based on the Intel’s Many Integrated Core (MIC) Architecture are emerging and adopted in many high-performance computer clusters. This paper introduces how to efficiently utilize Intel’s Xeon Phi multicore processors and MIC coprocessors for scalable geocomputation and geo-simulation by implementing two algorithms, Maximum Likelihood Classification (MLC) and Cellular Automata (CA), on supercomputer Beacon, a cluster of MICs. Four different programming models are examined, including (1) the native model, (2) the offload model, (3) the symmetric model, and (4) the hybrid-offload model. It can be concluded that while different kinds of parallel programming models can enable big data handling efficiently, the hybrid-offload model can achieve the best performance and scalability. These different programming models can be applied and extended to other types of geocomputation to handle big earth data.

ACS Style

Chenggang Lai; Xuan Shi; Miaoqing Huang. Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth data. Big Earth Data 2018, 2, 65 -85.

AMA Style

Chenggang Lai, Xuan Shi, Miaoqing Huang. Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth data. Big Earth Data. 2018; 2 (1):65-85.

Chicago/Turabian Style

Chenggang Lai; Xuan Shi; Miaoqing Huang. 2018. "Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth data." Big Earth Data 2, no. 1: 65-85.

Book chapter
Published: 12 May 2017 in Encyclopedia of GIS
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ACS Style

Xuan Shi; Miaoqing Huang. GPGPU in GIS. Encyclopedia of GIS 2017, 797 -805.

AMA Style

Xuan Shi, Miaoqing Huang. GPGPU in GIS. Encyclopedia of GIS. 2017; ():797-805.

Chicago/Turabian Style

Xuan Shi; Miaoqing Huang. 2017. "GPGPU in GIS." Encyclopedia of GIS , no. : 797-805.

Book chapter
Published: 12 May 2017 in Encyclopedia of GIS
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ACS Style

Xuan Shi; Miaoqing Huang. MIC in GIS. Encyclopedia of GIS 2017, 1226 -1232.

AMA Style

Xuan Shi, Miaoqing Huang. MIC in GIS. Encyclopedia of GIS. 2017; ():1226-1232.

Chicago/Turabian Style

Xuan Shi; Miaoqing Huang. 2017. "MIC in GIS." Encyclopedia of GIS , no. : 1226-1232.

Conference paper
Published: 05 January 2017 in Advances in Geographic Information Science
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Introduced in 2007, affinity propagation (AP) is a relatively new machine learning algorithm for unsupervised classification that has seldom been applied in geospatial applications. One bottleneck is that AP could hardly handle large data, and a serial computer program would take a long time to complete an AP calculation. New multicore and manycore computer architectures, combined with application accelerators, show promise for achieving scalable geocomputation by exploiting task and data levels of parallelism. This chapter introduces our recent progress in parallelizing the AP algorithm on a graphics processing unit (GPU) for spatial cluster analysis, the potential of the proposed solution to process big geospatial data, and its broader impact for the GIScience community.

ACS Style

Xuan Shi. Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data. Advances in Geographic Information Science 2017, 2017, 355 -369.

AMA Style

Xuan Shi. Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data. Advances in Geographic Information Science. 2017; 2017 ():355-369.

Chicago/Turabian Style

Xuan Shi. 2017. "Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data." Advances in Geographic Information Science 2017, no. : 355-369.

Book chapter
Published: 29 September 2016 in Encyclopedia of GIS
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CPU scaling showing transistor density, power consumption, and efficiency (Sutter, 2005) Due to the physical barrier, however, it could be remarkably difficult to achieve significant performance improvements by further increasing the clock frequency on the uniprocessors. Clock frequency of the chip is the number of clock cycles r ...

ACS Style

Xuan Shi; Miaoqing Huang. MIC in GIS. Encyclopedia of GIS 2016, 1 -6.

AMA Style

Xuan Shi, Miaoqing Huang. MIC in GIS. Encyclopedia of GIS. 2016; ():1-6.

Chicago/Turabian Style

Xuan Shi; Miaoqing Huang. 2016. "MIC in GIS." Encyclopedia of GIS , no. : 1-6.

Journal article
Published: 21 September 2016 in International Journal of Environmental Research and Public Health
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In response to the widespread concern about the adequacy, distribution, and disparity of access to a health care workforce, the correct identification of physicians’ practice locations is critical to access public health services. In prior literature, little effort has been made to detect and resolve the uncertainty about whether the address provided by a physician in the survey is a practice address or a home address. This paper introduces how to identify the uncertainty in a physician’s practice location through spatial analytics, text mining, and visual examination. While land use and zoning code, embedded within the parcel datasets, help to differentiate resident areas from other types, spatial analytics may have certain limitations in matching and comparing physician and parcel datasets with different uncertainty issues, which may lead to unforeseen results. Handling and matching the string components between physicians’ addresses and the addresses of the parcels could identify the spatial uncertainty and instability to derive a more reasonable relationship between different datasets. Visual analytics and examination further help to clarify the undetectable patterns. This research will have a broader impact over federal and state initiatives and policies to address both insufficiency and maldistribution of a health care workforce to improve the accessibility to public health services.

ACS Style

Xuan Shi; Bowei Xue; Imam M. Xierali. Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining. International Journal of Environmental Research and Public Health 2016, 13, 930 .

AMA Style

Xuan Shi, Bowei Xue, Imam M. Xierali. Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining. International Journal of Environmental Research and Public Health. 2016; 13 (9):930.

Chicago/Turabian Style

Xuan Shi; Bowei Xue; Imam M. Xierali. 2016. "Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining." International Journal of Environmental Research and Public Health 13, no. 9: 930.

Book chapter
Published: 26 August 2016 in Encyclopedia of GIS
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Before 1999, the Graphics Processing Unit (GPU) did not exist as graphics on the personal computer were manipulated by a video graphics array (VGA) controller (Blythe 2008; Nickolls and Kirk 2012). NVIDIA’s GeForce 256 was released in October 1999 as the first GPU in the world, while GPU was the term to denote that the graphics device had become a processor. Originally GPU was designed to process and generate computer graphics, images, and video games. When high quality and resolution graphics are expected in varieties of applications, in order to process large volume of pixels or vertices or geometries efficiently, hundreds of GPU cores and thousands of threads have to be developed and deployed accordingly. Consequently new generations of GPUs are massively parallel programmable processors that can be used for general purpose scientific computation. General-purpose computing on graphics processing units (GPGPU) is thus a more specific term in high performance comp ...

ACS Style

Xuan Shi; Miaoqing Huang. GPGPU in GIS. Encyclopedia of GIS 2016, 1 -8.

AMA Style

Xuan Shi, Miaoqing Huang. GPGPU in GIS. Encyclopedia of GIS. 2016; ():1-8.

Chicago/Turabian Style

Xuan Shi; Miaoqing Huang. 2016. "GPGPU in GIS." Encyclopedia of GIS , no. : 1-8.

Journal article
Published: 20 July 2016 in International Journal of Digital Earth
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This paper introduces how to automatically derive a minimum set of viewpoints for maximum coverage over a large scale of digital terrain data. This is a typical data and computation-intensive research covering a series of geocomputation tasks that have not been implemented efficiently or optimally in prior works. This paper introduces a three-step computational solution to resolve the problem. For any given digital elevation model (DEM) data, automatic generation of control viewpoints is the first step through map algebra calculation and hydrological modeling approaches. For each viewpoint, the viewshed calculation then has to be implemented. The combined viewshed derived from the viewshed of all viewpoints establishes the maximum viewshed coverage of the given DEM. Finally, detecting the minimum set of viewpoints for the maximum coverage is a Non-deterministic Polynomial-time hard problem. The outcome of the computation has broader societal impacts since the research questions and solutions can be adapted into real-world application and decision-making practice, such as the distribution, optimization and management of telecommunication infrastructure and wildfire observation towers, and military tactics and operations dependent upon landscape and terrain features.

ACS Style

Xuan Shi; Bowei Xue. Deriving a minimum set of viewpoints for maximum coverage over any given digital elevation model data. International Journal of Digital Earth 2016, 9, 1153 -1167.

AMA Style

Xuan Shi, Bowei Xue. Deriving a minimum set of viewpoints for maximum coverage over any given digital elevation model data. International Journal of Digital Earth. 2016; 9 (12):1153-1167.

Chicago/Turabian Style

Xuan Shi; Bowei Xue. 2016. "Deriving a minimum set of viewpoints for maximum coverage over any given digital elevation model data." International Journal of Digital Earth 9, no. 12: 1153-1167.

Conference
Published: 01 June 2015 in 2015 23rd International Conference on Geoinformatics
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The spatial distribution of physicians has a significant impact in public health research. It is critical to clarify whether the addresses provided by the physicians are the home addresses or the practice addresses, since the practice address is the key to understand relevant issues of maldistribution, accessibility and disparity. Through a pilot study as partial effort of the research project “Reducing Physician Distribution Uncertainty in Spatial Accessibility Research” sponsored by the National Institutes of Health (NIH award number 1R21CA182874-01), appropriate solutions were developed to differentiate the home addresses from practice addresses. This paper introduces how to understand the clustering patterns in physician distribution through Affinity Propagation, a relatively new clustering algorithm, to derive the potential extent of the practice locations for those physicians who provided home addresses. The physician data is derived from the 2014 American Medical Association (AMA) Physician Masterfile, while two counties (Fulton and DeKalb) in the metropolitan area of Atlanta, Georgia were selected as the study area. Both Euclidian distance and driving distance were applied in the AP algorithm, while gravity models based AP calculation were applied in comparison to the clustering of individual physicians. By justifying preference and similarity parameters in the AP calculation, hierarchical clustering patterns can be derived and perceived. Future research challenges in AP clustering are identified, while this pilot study can be extended with broader impact in public health research.

ACS Style

Xuan Shi; Bowei Xue; Imam Xierali. Understanding the clustering patterns in physician distribution through Affinity Propagation. 2015 23rd International Conference on Geoinformatics 2015, 2015, 1 -5.

AMA Style

Xuan Shi, Bowei Xue, Imam Xierali. Understanding the clustering patterns in physician distribution through Affinity Propagation. 2015 23rd International Conference on Geoinformatics. 2015; 2015 ():1-5.

Chicago/Turabian Style

Xuan Shi; Bowei Xue; Imam Xierali. 2015. "Understanding the clustering patterns in physician distribution through Affinity Propagation." 2015 23rd International Conference on Geoinformatics 2015, no. : 1-5.

Research article
Published: 13 April 2015 in The International Journal of High Performance Computing Applications
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Coprocessors based on the Intel Many Integrated Core (MIC) Architecture have been adopted in many high-performance computer clusters. Typical parallel programming models, such as MPI and OpenMP, are supported on MIC processors to achieve the parallelism. In this work, we conduct a detailed study on the performance and scalability of the MIC processors under different programming models using the Beacon computer cluster. Our findings are as follows. (1) The native MPI programming model on the MIC processors is typically better than the offload programming model, which offloads the workload to MIC cores using OpenMP. (2) On top of the native MPI programming model, multithreading inside each MPI process can further improve the performance for parallel applications on computer clusters with MIC coprocessors. (3) Given a fixed number of MPI processes, it is a good strategy to schedule these MPI processes to as few MIC processors as possible to reduce the cross-processor communication overhead. (4) The hybrid MPI programming model, in which data processing is distributed to both MIC cores and CPU cores, can outperform the native MPI programming model.

ACS Style

Miaoqing Huang; Chenggang Lai; Xuan Shi; Zhijun Hao; Haihang You. Study of parallel programming models on computer clusters with Intel MIC coprocessors. The International Journal of High Performance Computing Applications 2015, 31, 303 -315.

AMA Style

Miaoqing Huang, Chenggang Lai, Xuan Shi, Zhijun Hao, Haihang You. Study of parallel programming models on computer clusters with Intel MIC coprocessors. The International Journal of High Performance Computing Applications. 2015; 31 (4):303-315.

Chicago/Turabian Style

Miaoqing Huang; Chenggang Lai; Xuan Shi; Zhijun Hao; Haihang You. 2015. "Study of parallel programming models on computer clusters with Intel MIC coprocessors." The International Journal of High Performance Computing Applications 31, no. 4: 303-315.

Journal article
Published: 11 September 2014 in Transactions in GIS
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Emerging computer architectures and systems that combine multi‐core CPUs and accelerator technologies, like many‐core Graphic Processing Units (GPUs) and Intel's Many Integrated Core (MIC) coprocessors, would provide substantial computing power for many time‐consuming spatial‐temporal computation and applications. Although a distributed computing environment is suitable for large‐scale geospatial computation, emerging advanced computing infrastructure remains unexplored in GIScience applications. This article introduces three categories of geospatial applications by effectively exploiting clusters of CPUs, GPUs and MICs for comparative analysis. Within these three benchmark tests, the GPU clusters exemplify advantages in the use case of embarrassingly parallelism. For spatial computation that has light communication between the computing nodes, GPU clusters present a similar performance to that of the MIC clusters when large data is applied. For applications that have intensive data communication between the computing nodes, MIC clusters could display better performance than GPU clusters. This conclusion will be beneficial to the future endeavors of the GIScience community to deploy the emerging heterogeneous computing infrastructure efficiently to achieve high or better performance spatial computation over big data.

ACS Style

Xuan Shi; Chenggang Lai; Miaoqing Huang; Haihang You. Geocomputation over the Emerging Heterogeneous Computing Infrastructure. Transactions in GIS 2014, 18, 3 -24.

AMA Style

Xuan Shi, Chenggang Lai, Miaoqing Huang, Haihang You. Geocomputation over the Emerging Heterogeneous Computing Infrastructure. Transactions in GIS. 2014; 18 ():3-24.

Chicago/Turabian Style

Xuan Shi; Chenggang Lai; Miaoqing Huang; Haihang You. 2014. "Geocomputation over the Emerging Heterogeneous Computing Infrastructure." Transactions in GIS 18, no. : 3-24.

Articles
Published: 24 April 2014 in GIScience & Remote Sensing
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The iterative self-organizing data analysis technique algorithm (ISODATA) was implemented over supercomputers Kraken, Keeneland and Beacon to explore scalable and high-performance solutions for image processing and analytics using emerging advanced computer architectures. When 10 classes are extracted from one 18-GB image tile, the calculation can be reduced from several hours to no more than 90 seconds when 100 CPU, GPU or MIC processors are utilized. High-performance scalability tests were further implemented over Kraken using 10,800 processors to extract various number of classes from 12 image tiles totalling 216 gigabytes. As the first geospatial computations over GPU clusters (Keeneland) and MIC clusters (Beacon), the success of this research illustrates a solid foundation for exploring the potential of scalable and high-performance geospatial computation for the next generation cyber-enabled image analytics.

ACS Style

Xuan Shi; Miaoqing Huang; Haihang You; Chenggang Lai; Zhong Chen. Unsupervised image classification over supercomputers Kraken, Keeneland and Beacon. GIScience & Remote Sensing 2014, 51, 321 -338.

AMA Style

Xuan Shi, Miaoqing Huang, Haihang You, Chenggang Lai, Zhong Chen. Unsupervised image classification over supercomputers Kraken, Keeneland and Beacon. GIScience & Remote Sensing. 2014; 51 (3):321-338.

Chicago/Turabian Style

Xuan Shi; Miaoqing Huang; Haihang You; Chenggang Lai; Zhong Chen. 2014. "Unsupervised image classification over supercomputers Kraken, Keeneland and Beacon." GIScience & Remote Sensing 51, no. 3: 321-338.

Book chapter
Published: 25 September 2013 in Modern Accelerator Technologies for Geographic Information Science
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Simulating urban land-use changes involves both high modeling and computational complexities. This paper focuses on a typical spatio-temporal modeling method that has been commonly used in urban land-use change studies—Cellular Automata (CA). After reviewing the recent development of utilizing various parallel computing technologies (e.g., computer clusters and Graphics Processing Unit [GPU]) in CA-based urban models, this paper presents a pilot study, in which a classical CA model, the Game of Life, was implemented as a parallel program over the GPU/CPU heterogeneous cluster architecture, and 300+ speed-up was achieved using 20 GPUs. In conclusion, emerging high-performance computing technologies, such as GPU/CPU heterogeneous cluster architecture, provide promising potentials to overcome the computing obstacle of urban land-use change models, and enable researchers to examine, validate and advance urban land-use change theories and derive sound urban planning strategies. To efficiently utilize the computing power of the GPU/CPU clusters, hybrid parallelism must be implemented to coordinate the computing among GPU/CPU nodes, as well as among the threads on each GPU. However, implementing such hybrid parallelism is challenging for its high development complexity.

ACS Style

Qingfeng Guan; Xuan Shi. Opportunities and Challenges for Urban Land-Use Change Modeling Using High-Performance Computing. Modern Accelerator Technologies for Geographic Information Science 2013, 227 -236.

AMA Style

Qingfeng Guan, Xuan Shi. Opportunities and Challenges for Urban Land-Use Change Modeling Using High-Performance Computing. Modern Accelerator Technologies for Geographic Information Science. 2013; ():227-236.

Chicago/Turabian Style

Qingfeng Guan; Xuan Shi. 2013. "Opportunities and Challenges for Urban Land-Use Change Modeling Using High-Performance Computing." Modern Accelerator Technologies for Geographic Information Science , no. : 227-236.

Book chapter
Published: 25 September 2013 in Modern Accelerator Technologies for Geographic Information Science
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The rich details of space-time complexity in social science remain largely unexplored because of the challenge of intensities of data and computing. The current space-time simulation and statistics for social science research can only deal with a limited amount of data. We introduce a pilot study about how to deploy the modern accelerator technology and hybrid computer systems to extend the National Institute of Justice-funded Near-repeat calculation, a typical social science application? This pilot study demonstrates that it is promising to leverage high performance computing for solving large-scale space-time interaction problems, which has long been a challenging statistical issue for spatiotemporally integrated social science.

ACS Style

Xinyue Ye; Xuan Shi. Pursuing Spatiotemporally Integrated Social Science Using Cyberinfrastructure. Modern Accelerator Technologies for Geographic Information Science 2013, 215 -226.

AMA Style

Xinyue Ye, Xuan Shi. Pursuing Spatiotemporally Integrated Social Science Using Cyberinfrastructure. Modern Accelerator Technologies for Geographic Information Science. 2013; ():215-226.

Chicago/Turabian Style

Xinyue Ye; Xuan Shi. 2013. "Pursuing Spatiotemporally Integrated Social Science Using Cyberinfrastructure." Modern Accelerator Technologies for Geographic Information Science , no. : 215-226.

Book chapter
Published: 25 September 2013 in Modern Accelerator Technologies for Geographic Information Science
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Geographic Information System (GIS) enables heterogeneous geospatial data integration, processing, analysis, and visualization. With a variety of software tools, GIS makes substantial contribution to the advancements of science, engineering and decision-making in geospatial-related natural and social sciences, public safety and emergency response, spatial intelligence analytics and military operations, ecological and environmental science and engineering, and public health. Geospatial data represents real-world geographic features or objects using either vector or raster data models. In the vector model, features are captured as discrete geometric objects and represented as points, lines or polygons with non-spatial attributes. In the raster model, features are represented on a grid, or as a multidimensional matrix, including satellite imagery and other remotely sensed data.

ACS Style

Xuan Shi; Volodymyr Kindratenko; Chaowei Yang. Modern Accelerator Technologies for Geographic Information Science. Modern Accelerator Technologies for Geographic Information Science 2013, 3 -6.

AMA Style

Xuan Shi, Volodymyr Kindratenko, Chaowei Yang. Modern Accelerator Technologies for Geographic Information Science. Modern Accelerator Technologies for Geographic Information Science. 2013; ():3-6.

Chicago/Turabian Style

Xuan Shi; Volodymyr Kindratenko; Chaowei Yang. 2013. "Modern Accelerator Technologies for Geographic Information Science." Modern Accelerator Technologies for Geographic Information Science , no. : 3-6.

Book chapter
Published: 25 September 2013 in Modern Accelerator Technologies for Geographic Information Science
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Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) is commonly used for unsupervised image classification in remote sensing applications. Although parallelized approaches were explored, previous works mostly utilized the power of CPU clusters. We deploy the many-cores in the Graphics Processing Unit (GPU) to accelerate the unsupervised image classification over GPU. The proposed solution is scalable and satisfactory to speed up the computational time, while the quality of classification is almost the same as that from ERDAS, a well known remote sensing software.

ACS Style

Fei Ye; Xuan Shi. Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU. Modern Accelerator Technologies for Geographic Information Science 2013, 145 -156.

AMA Style

Fei Ye, Xuan Shi. Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU. Modern Accelerator Technologies for Geographic Information Science. 2013; ():145-156.

Chicago/Turabian Style

Fei Ye; Xuan Shi. 2013. "Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU." Modern Accelerator Technologies for Geographic Information Science , no. : 145-156.

Journal article
Published: 23 September 2013 in ISPRS International Journal of Geo-Information
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Web service is a technological solution for software interoperability that supports the seamless integration of diverse applications. In the vision of web service architecture, web services are described by the Web Service Description Language (WSDL), discovered through Universal Description, Discovery and Integration (UDDI) and communicate by the Simple Object Access Protocol (SOAP). Such a divination has never been fully accomplished yet. Although it was criticized that WSDL only has a syntactic definition of web services, but was not semantic, prior initiatives in semantic web services did not establish a correct methodology to resolve the problem. This paper examines the distinction and relationship between the syntactic and semantic definitions for web services that characterize different purposes in service computation. Further, this paper proposes that the semantics of web service are neutral and independent from the service interface definition, data types and platform. Such a conclusion can be a universal law in software engineering and service computing. Several use cases in the GIScience application are examined in this paper, while the formalization of geospatial services needs to be constructed by the GIScience community towards a comprehensive ontology of the conceptual definitions and relationships for geospatial computation. Advancements in semantic web services research will happen in domain science applications.

ACS Style

Xuan Shi. The Semantics of Web Services: An Examination in GIScience Applications. ISPRS International Journal of Geo-Information 2013, 2, 888 -907.

AMA Style

Xuan Shi. The Semantics of Web Services: An Examination in GIScience Applications. ISPRS International Journal of Geo-Information. 2013; 2 (3):888-907.

Chicago/Turabian Style

Xuan Shi. 2013. "The Semantics of Web Services: An Examination in GIScience Applications." ISPRS International Journal of Geo-Information 2, no. 3: 888-907.

Journal article
Published: 01 April 2013 in GIScience & Remote Sensing
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ACS Style

Xuan Shi; Fei Ye. Kriging interpolation over heterogeneous computer architectures and systems. GIScience & Remote Sensing 2013, 50, 196 -211.

AMA Style

Xuan Shi, Fei Ye. Kriging interpolation over heterogeneous computer architectures and systems. GIScience & Remote Sensing. 2013; 50 (2):196-211.

Chicago/Turabian Style

Xuan Shi; Fei Ye. 2013. "Kriging interpolation over heterogeneous computer architectures and systems." GIScience & Remote Sensing 50, no. 2: 196-211.