21,140 research outputs found
Analogy Mining for Specific Design Needs
Finding analogical inspirations in distant domains is a powerful way of
solving problems. However, as the number of inspirations that could be matched
and the dimensions on which that matching could occur grow, it becomes
challenging for designers to find inspirations relevant to their needs.
Furthermore, designers are often interested in exploring specific aspects of a
product-- for example, one designer might be interested in improving the
brewing capability of an outdoor coffee maker, while another might wish to
optimize for portability. In this paper we introduce a novel system for
targeting analogical search for specific needs. Specifically, we contribute a
novel analogical search engine for expressing and abstracting specific design
needs that returns more distant yet relevant inspirations than alternate
approaches
Automatic vs Manual Provenance Abstractions: Mind the Gap
In recent years the need to simplify or to hide sensitive information in
provenance has given way to research on provenance abstraction. In the context
of scientific workflows, existing research provides techniques to semi
automatically create abstractions of a given workflow description, which is in
turn used as filters over the workflow's provenance traces. An alternative
approach that is commonly adopted by scientists is to build workflows with
abstractions embedded into the workflow's design, such as using sub-workflows.
This paper reports on the comparison of manual versus semi-automated approaches
in a context where result abstractions are used to filter report-worthy results
of computational scientific analyses. Specifically; we take a real-world
workflow containing user-created design abstractions and compare these with
abstractions created by ZOOM UserViews and Workflow Summaries systems. Our
comparison shows that semi-automatic and manual approaches largely overlap from
a process perspective, meanwhile, there is a dramatic mismatch in terms of data
artefacts retained in an abstracted account of derivation. We discuss reasons
and suggest future research directions.Comment: Preprint accepted to the 2016 workshop on the Theory and Applications
of Provenance, TAPP 201
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