7,233 research outputs found
Towards a service-oriented e-infrastructure for multidisciplinary environmental research
Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures
Causality and the semantics of provenance
Provenance, or information about the sources, derivation, custody or history
of data, has been studied recently in a number of contexts, including
databases, scientific workflows and the Semantic Web. Many provenance
mechanisms have been developed, motivated by informal notions such as
influence, dependence, explanation and causality. However, there has been
little study of whether these mechanisms formally satisfy appropriate policies
or even how to formalize relevant motivating concepts such as causality. We
contend that mathematical models of these concepts are needed to justify and
compare provenance techniques. In this paper we review a theory of causality
based on structural models that has been developed in artificial intelligence,
and describe work in progress on a causal semantics for provenance graphs.Comment: Workshop submissio
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
The ability to replicate predictions by machine learning (ML) or artificial
intelligence (AI) models and results in scientific workflows that incorporate
such ML/AI predictions is driven by numerous factors. An uncertainty-aware
metric that can quantitatively assess the reproducibility of quantities of
interest (QoI) would contribute to the trustworthiness of results obtained from
scientific workflows involving ML/AI models. In this article, we discuss how
uncertainty quantification (UQ) in a Bayesian paradigm can provide a general
and rigorous framework for quantifying reproducibility for complex scientific
workflows. Such as framework has the potential to fill a critical gap that
currently exists in ML/AI for scientific workflows, as it will enable
researchers to determine the impact of ML/AI model prediction variability on
the predictive outcomes of ML/AI-powered workflows. We expect that the
envisioned framework will contribute to the design of more reproducible and
trustworthy workflows for diverse scientific applications, and ultimately,
accelerate scientific discoveries
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