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Paolo & Chris Break-out “Knowledge Units”

Paolo & Chris Break-out “Knowledge Units”. Title: Service enabling models routinely – Convener: Chris Title : Representing experiments as explicit & repeatable workflows – Convener: Paolo List of participants:

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Paolo & Chris Break-out “Knowledge Units”

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  1. Paolo & Chris Break-out “Knowledge Units” • Title: Service enabling models routinely – Convener: Chris • Title : Representing experiments as explicit & repeatable workflows – Convener: Paolo • List of participants: • Chris Hill, Richard Wadsworth, Mark Gahegan, Yang Ou, Patrick Maue, Yi Qiang, Femke Reitsma, Maciej Dabrowski, Paolo Besana. • Discussion: • Service is not equal to web-service. Includes web-service, RMI, java VM, humans…. • in Geo there is work being put into service enabling entities. This may be a nice opportunity to build in some K.I. foundation. • approach is to develop a process model (e.g. in OWL/S, WSML, GeoVISTA studio). • Challenges and approaches • Ill-constrained descriptions leading to overly large solution space. Addressed by adding “contrived” constraints). Workflows OK for abstract modeling but grounding can be tricky. • Describing interactions between arbitrary components has many degrees of freedom (natural language like complexity). Restrictions on form of components can help reduce complexity. • Time dependence (iteration) and complex control flow is hard with current generation workflow tools (OWL/S, WSML, BPL ….). Some examples of how to do this in WSML. • Equivalence between services is ill-posed. A service with the same pre- and post- conditions may not produce exactly the same result. This might be good in some contexts, but in others it may need other services that carry out “quality tests” on resulting output (are these expressible as post conditions). Context and metadata annotation for workflow are important to be able to “reason” about flows. • Recommendations • Target some narrow problems to show progress. • Developing use cases that capture current best practices in workflows (with context and metadata annotation) would be a good way to build a knowledge database and provide raw material for smart systems. Also be good (for science and PR) to add these to geo publications.

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