FOSS geospatial libraries in scientific workflow environments: experiences and directions

McFerren, Graeme; Zyl, Terence L. van; Vahed, Anwar

doi:10.1007/s12518-011-0062-0

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…This article discusses advances in the development of a software environment with capabilities for accessing, utilizing and incorporating geospatial data and processes into the scientific workflows of researchers interested in incorporating earth observation and geospatial analysis into their work. In previous work, we introduced the concept of geospatially enabled scientific workflows (McFerren et al 2010); this article shares progress made, insights gained and future research avenues exposed in the process of implementing EO4Vistrails, a geospatial extension to the open‐source VisTrails scientific workflow, provenance management, data exploration and visualization system. FOSS4G software and toolsets are heavily used in EO4Vistrails to provide spatial database, OGC Web Service, geospatial analysis, data transformation and visualization capabilities.…”

Section: Discussionmentioning

confidence: 99%

“…In previous work we introduced the concept of geospatially enabled scientific workflow environments where geospatial tools, functionalities and data are used in conjunction with other research tools, functionalities and data, from, for example, the visualization, numerical modelling and simulation, computational intelligence, high performance computing and statistical domains (McFerren et al. 2010).…”

Section: Designmentioning

confidence: 99%

“…We note that FOSS4G tools provide possibilities for geospatially enabling scientific workflows and allow opportunities for experimentation, which is vital to the scientific workflow process. The low cost and variety of FOSS4G tools available ofers functionality and flexibility for accomplishing scientific inquiry (McFerren et al. 2010).…”

Section: Designmentioning

confidence: 99%

“…This article uses the concept of geospatially enabled scientific workflows: a concept that is tangential to that of geospatial workflows and expanded upon in previous work by the authors. We subscribe to the following definition of a geospatially enabled scientific workflow (McFerren et al. 2012):…”

Section: Introductionmentioning

confidence: 99%

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Earth Observation Scientific Workflows in a Distributed Computing Environment

Zyl¹,

Vahed²,

McFerren³

et al. 2012

Transactions in GIS

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Geospatially Enabled Scientific Workflows offer a promising toolset to help researchers in the earth observation domain with many aspects of the scientific process. One such aspect is that of access to distributed earth observation data and computing resources. Earth observation research often utilizes large datasets requiring extensive CPU and memory resources in their processing. These resource intensive processes can be chained; the sequence of processes (and their provenance) makes up a scientific workflow. Despite the exponential growth in capacity of desktop computers, their resources are often insufficient for the scientific workflow processing tasks at hand. By integrating distributed computing capabilities into a geospatially enabled scientific workflow environment, it is possible to provide researchers with a mechanism to overcome the limitations of the desktop computer. Most of the effort on extending scientific workflows with distributed computing capabilities has focused on the web services approach, as exemplified by the OGC's Web Processing Service and by GRID computing. The approach to leveraging distributed computing resources described in this article uses instead remote objects via RPyC and the dynamic properties of the Python programming language. The Vistrails environment has been extended to allow for geospatial processing through the EO4Vistrails package (http://code.google.com/p/eo4vistrails/). In order to allow these geospatial processes to be seamlessly executed on distributed resources such as cloud computing nodes, the Vistrails environment has been extended with both multi‐tasking capabilities and distributed processing capabilities. The multi‐tasking capabilities are required in order to allow Vistrails to run side‐by‐side processes, a capability it does not currently have. The distributed processing capabilities are achieved through the use of remote objects and mobile code through RPyC.

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Section: Discussionmentioning

confidence: 99%

Section: Designmentioning

confidence: 99%

Section: Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Earth Observation Scientific Workflows in a Distributed Computing Environment

Zyl¹,

Vahed²,

McFerren³

et al. 2012

Transactions in GIS

Self Cite

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“…In the past scientific workflow systems were used extensively in the field of bioinformatics to not only capture analytical processes but also to document those processes amongst collaborators for review and revision (Sun et al, 2011). While scientific workflows are used extensively for non-spatial processes (Brown et al, 2007;Barseghian et al, 2010), their ability to conduct spatial operations are still in the initial stage of development (Callaghan et al, 2010;McFerren et al, 2012;Zyl et al, 2012;Yue et al, 2015;Scheider and Ballatore, 2017;Radosevic et al, 2020). Some researchers have already propounded the use of scientific workflows for spatial analytics (Ludäscher et al, 2006;Freire et al, 2018;Kitzes et al, 2018;Radosevic et al, 2020;Cerutti et al, 2021).…”

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confidence: 99%

K-span: Open and reproducible spatial analytics using scientific workflows

Forkan

Both²,

Bellman³

et al. 2023

Front. Earth Sci.

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This paper describes the design, development, and testing of a general-purpose scientific-workflows tool for spatial analytics. Spatial analytics processes are frequently complex, both conceptually and computationally. Adaptation, documention, and reproduction of bespoke spatial analytics procedures represents a growing challenge today, particularly in this era of big spatial data. Scientific workflow systems hold the promise of increased openness and transparency with improved automation of spatial analytics processes. In this work, we built and implemented a KNIME spatial analytics (“K-span”) software tool, an extension to the general-purpose open-source KNIME scientific workflow platform. The tool augments KNIME with new spatial analytics nodes by linking to and integrating a range of existing open-source spatial software and libraries. The implementation of the K-span system is demonstrated and evaluated with a case study associated with the original process of construction of the Australian national DEM (Digital Elevation Model) in the Greater Brisbane area of Queensland, Australia by Geoscience Australia (GA). The outcomes of translating example spatial analytics process into a an open, transparent, documented, automated, and reproducible scientific workflow highlights the benefits of using our system and our general approach. These benefits may help in increasing users’ assurance and confidence in spatial data products and in understanding of the provenance of foundational spatial data sets across diverse uses and user groups.

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