A Web Service Model for Climate Data Access on the Grid

Woolf, Andrew; Haines, Keith

doi:10.1177/1094342003173002

Cited by 22 publications

(10 citation statements)

References 5 publications

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“…In this ecosystem are the netCDF Operators (NCO) -software for manipulation and analysis of gridded geoscience data stored in the self-describing netCDF format. NCO is used in several niches in geoscience data analysis workflow (Woolf et al, 2003), because its functionality is independent of and complementary to data discovery, aggregation, and dissemination.…”

Section: Introductionmentioning

confidence: 99%

Analysis of self-describing gridded geoscience data with netCDF Operators (NCO)

Zender

2008

Environmental Modelling & Software

162

107

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Arithmetic Metadata Model design netCDF a b s t r a c tThe netCDF Operator (NCO) software facilitates manipulation and analysis of gridded geoscience data stored in the self-describing netCDF format. NCO is optimized to efficiently analyze large multi-dimensional data sets spanning many files. Researchers and data centers often use NCO to analyze and serve observed and modeled geoscience data including satellite observations and weather, air quality, and climate forecasts. NCO's functionality includes shared memory threading, a message-passing interface, network transparency, and an interpreted language parser. NCO treats data files as a high level data type whose contents may be simultaneously manipulated by a single command. Institutions and data portals often use NCO for middleware to hyperslab and aggregate data set requests, while scientific researchers use NCO to perform three general functions: arithmetic operations, data permutation and compression, and metadata editing. We describe NCO's design philosophy and primary features, illustrate techniques to solve common geoscience and environmental data analysis problems, and suggest ways to design gridded data sets that can ease their subsequent analysis.

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Section: Introductionmentioning

confidence: 99%

Analysis of self-describing gridded geoscience data with netCDF Operators (NCO)

Zender

2008

Environmental Modelling & Software

162

107

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“…A web service for accessing gridded climate data Woolf et al (2003) described a web service (GADS) for accessing climate model data in Grid applications. The service has two operations: r dataQuery(): used for metadata queries on available datasets, variables, and gridded spatiotemporal domains of variables r dataRequest(): used for requesting a subset of data in a specified file format (netCDF, HDF, GRIB, raw binary)…”

Section: Wsdl Bindingsmentioning

confidence: 99%

Standards‐based data interoperability in the climate sciences

Woolf¹,

Cramer

Gutierrez

et al. 2005

Meteorological Applications

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“…A set of operations is often necessary to provide an appropriate solution to complex scientific applications. The use of Web services technologies provides valuable solutions to speed up the scientific data analysis [6], [7]. A composition of a set of services as a composite service can be reused by other researchers.…”

Section: Introductionmentioning

confidence: 99%

Bio‐inspired cost‐aware optimization for data‐intensive service provision

Wang

Shen

Luo

2015

Concurrency and Computation

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The use of Big Data and the development of cloud computing have led to greater dependence on dataintensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss singleobjective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics. The use of Big Data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss single-objective and multi-objective data-intensive service provision problems based on bio-inspired algorithms. Further, we will propose an ant-inspired negotiation approach. Finally, this paper points out future research topics. Disciplines Engineering | Science and Technology Studies

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A Web Service Model for Climate Data Access on the Grid

Cited by 22 publications

References 5 publications

Analysis of self-describing gridded geoscience data with netCDF Operators (NCO)

Analysis of self-describing gridded geoscience data with netCDF Operators (NCO)

Standards‐based data interoperability in the climate sciences

Bio‐inspired cost‐aware optimization for data‐intensive service provision

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