Building Model as a Service to support geosciences

Li, Zhenlong; Yang, Chaowei; Huang, Qunying; Liu, Kai; Sun, Min; Xia, Jizhe

doi:10.1016/j.compenvurbsys.2014.06.004

Cited by 39 publications

(42 citation statements)

References 29 publications

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“…The comprehensive solution provided to users has exhibited an efficient approach on how to use and manage remote sensing data to support their geospatial applications and projects. (Li, Yang, and Huang 2014).…”

Section: Discussionmentioning

confidence: 99%

The TripleSat constellation: a new geospatial data service model

Wen

Jiang

Guan

et al. 2017

Geo-spatial Information Science

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a 21st century aerospace technology co., ltd., Beijing, china; b 21st century aerospace technology (asia) pte. ltd., singapore ABSTRACTWith the increase of different sensors, applications and customers, the demand from data providers and users is for a new geospatial data service model, which supports low cost, high dexterity, and which would provide a comprehensive service. Based on such requirements and demands, the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise, Twenty First Century Aerospace Technology Co., Ltd. (21AT). The company is the first commercial Earth observation satellite operator and service provider in China. This new geospatial data service model allows the user to directly access multi-source satellite data, manage the data order, and carry out automatic massive data production and delivery. The solution also implements safe and hierarchical user management, statistical data analysis, and automatic information reports. In addition, a mobile application is also available for users to easily access system functions. This new geospatial solution has already been successfully applied and installed in many customer sites in China, and is now available globally for international clients interested in fast geospatial solutions. It enables the success of customers' operational services. Besides providing TripleSat Constellation images, the multi-source data access system also allows the users to access other satellite data sources, based on customized agreement. This paper describes and discusses this new geospatial data service model.

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Section: Discussionmentioning

confidence: 99%

The TripleSat constellation: a new geospatial data service model

Wen

Jiang

Guan

et al. 2017

Geo-spatial Information Science

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“…Nativi et al (2013) introduced the Group on Earth Observation (GEO) Model Web initiative and discussed the basic principles and technical challenges of implementing a Model Web. By employing state of the art of cloud computing, Li et al (2014) presented methodologies for designing and implementing Model as a Service (MaaS). The heterogeneity of data formats is an important issue in the sharing of geographical models (Crosier et al 2003;Feng et al 2009Feng et al , 2011Li et al 2014) because geographical models and data are highly coupled (Voinov and Cerco 2010;Granell et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…By employing state of the art of cloud computing, Li et al (2014) presented methodologies for designing and implementing Model as a Service (MaaS). The heterogeneity of data formats is an important issue in the sharing of geographical models (Crosier et al 2003;Feng et al 2009Feng et al , 2011Li et al 2014) because geographical models and data are highly coupled (Voinov and Cerco 2010;Granell et al 2010). Users must prepare data strictly according to the data format requirements of geographical models (Feng et al 2009;Yue et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Describing data formats of geographical models

Guo

et al. 2015

Environ Earth Sci

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The variations that characterise the design and implementation of geographical models create significant difficulties in the sharing of models. One such difficulty concerns the fact that geographical models and data are highly coupled, and various heterogeneous data formats exist. This results in a laborious and unending data format conversion process during model sharing. In this paper, data format is defined to include three components: data type, structure and layout. A new method is proposed to clearly describe data formats of geographical models in a universal manner in an attempt to solve the problem identified above. First, the characteristics of geographical models, data and their formats were analysed. A concept model of data formats was then proposed, focusing on data location, data types and separators. A novel data format description language called the Data Format Markup Language (DFML) was designed accordingly. DFML uses XML markup elements to describe format information. The primary markup elements and syntax rules of DFML are introduced, and the use of DFML to describe data formats is explained step by step. Finally, case studies were performed to test this approach with two types of data formats: (1) data formats of a watershed model: Soil and Water Assessment Tool (SWAT), and (2) typical GIS data formats, namely, the ESRI ASCII Raster and shapefile formats. The results of these case studies demonstrated that DFML is easy to understand and use and can adequately describe data formats of geographical models.

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“…As cloud computing and Hadoop are increasingly adopted in addressing the big data and computational problems in geospatial domains (e.g., [31][32][33][34]), how to optimize the allocated computing resources by considering the dynamic geoprocessing workload deserves investigation. However, there is little research to study dynamically scaling a Hadoop cluster in cloud environments.…”

Section: Auto-sscaling Hadoop In the Cloudmentioning

confidence: 99%

Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data

Yang

Liu

et al. 2016

IJGI

Self Cite

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Efficient processing of big geospatial data is crucial for tackling global and regional challenges such as climate change and natural disasters, but it is challenging not only due to the massive data volume but also due to the intrinsic complexity and high dimensions of the geospatial datasets. While traditional computing infrastructure does not scale well with the rapidly increasing data volume, Hadoop has attracted increasing attention in geoscience communities for handling big geospatial data. Recently, many studies were carried out to investigate adopting Hadoop for processing big geospatial data, but how to adjust the computing resources to efficiently handle the dynamic geoprocessing workload was barely explored. To bridge this gap, we propose a novel framework to automatically scale the Hadoop cluster in the cloud environment to allocate the right amount of computing resources based on the dynamic geoprocessing workload. The framework and auto-scaling algorithms are introduced, and a prototype system was developed to demonstrate the feasibility and efficiency of the proposed scaling mechanism using Digital Elevation Model (DEM) interpolation as an example. Experimental results show that this auto-scaling framework could (1) significantly reduce the computing resource utilization (by 80% in our example) while delivering similar performance as a full-powered cluster; and (2) effectively handle the spike processing workload by automatically increasing the computing resources to ensure the processing is finished within an acceptable time. Such an auto-scaling approach provides a valuable reference to optimize the performance of geospatial applications to address data-and computational-intensity challenges in GIScience in a more cost-efficient manner.

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Building Model as a Service to support geosciences

Cited by 39 publications

References 29 publications

The TripleSat constellation: a new geospatial data service model

The TripleSat constellation: a new geospatial data service model

Describing data formats of geographical models

Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data

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