Scrum is the most adopted Agile methodology. The research conducted on Scrum adoption is mainly qualitative and there is therefore a need for a quantitative study on Scrum adoption challenges. The primary objective of this paper is to present the findings of a study on the factors that have a significant relationship with Scrum adoption as perceived by Scrum practitioners working within South African organizations. Towards this objective, a narrative review to extract and synthesize the existing challenges was conducted. These synthesized challenges were used in the development of a conceptual framework for evaluating the challenges that have a correlation and linear relationship with Scrum adoption. Following this, a survey questionnaire was used to test and evaluate the factors forming part of the developed framework. The findings indicate that Relative Advantage, Complexity, and Sprint Management are factors that have a significant linear relationship with Scrum adoption. Our recommendation is that organizations consider these findings during their adoption phase of Scrum.

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Scientific Workflows provides a technology that facilitates researchers by allowing them to capture in a machine processable manner the method relating to some research. This increases both provenance and repeatability of the research and allows for increased collaboration through workflow sharing. The Sensor Web is an open complex adaptive system the pervades the internet and provides access to sensor resources. One mechanism for describing sensor resources is through the use of SensorML. It is shown that SensorML can be used as a mechanism for describing Scientific Workflows and thus facilitates completely distributed workflow descriptions on the web. However in order to fully capture the requirements as relating to open and distributed environments some extensions to SensorML will be required.

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.