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On integrating scientific resources through semantic registration

In many data-centric scientific applications it is common to register datasets and computational services with a federation registry (also commonly called a catalog, directory, or repository). For example, the scientific data-handling system under development in the SEEK project must consider various dataset registries, including: MCAT, for access to SRB-registered datasets Metacat, for KNB-registered datasets DiGIR, for UDDI-registered data and Xanthoria, an XML-based data registry.

Towards a generic framework for semantic registration of scientific data

this paper, we consider the specific problem of registering scientific data (as opposed to arbitrary Web content) with ontologies. We propose a generic framework to support semantic registration of scientific datasets, which we intend to deploy in the SEEK project---a multidisciplinary effort to help scientists discover, access, integrate, and analyze distributed ecological information.

An ontology-driven framework for data transformation in scientific workflows

Ecologists spend considerable effort integrating heterogeneous data for statistical analyses and simulations, for example, to run and test predictive models. Our research is focused on reducing this effort by providing data integration and transformation tools, allowing researchers to focus on “real science,” that is, discovering new knowledge through analysis and modeling. This paper defines a generic framework for transforming heterogeneous data within scientific workflows. Our approach relies on a formalized ontology, which serves as a simple, unstructured global schema.

Actor-oriented design of scientific workflows

Scientific workflows are becoming increasingly important as a unifying mechanism for interlinking scientific data management, analysis, simulation, and visualization tasks. Scientific workflow systems are problem-solving environments, supporting scientists in the creation and execution of scientific workflows. While current systems permit the creation of executable workflows, conceptual modeling and design of scientific workflows has largely been neglected.

Towards automatic generation of semantic types in scientific workflows

Scientific workflow systems are problem-solving environments that allow scientists to automate and reproduce data management and analysis tasks. Workflow components include actors (e.g., queries, transformations, analyses, simulations, visualizations), and datasets which are produced and consumed by actors. The increasing number of such components creates the problem of discovering suitable components and of composing them to form the desired scientific workflow. In previous work we proposed the use of semantic types (annotations relative to an ontology) to solve these problems.

A calculus for propagating semantic annotations through scientific workflow queries

Scientific workflows facilitate automation, reuse, and reproducibility of scientific data management and analysis tasks. Scientific workflows are often modeled as dataflow networks, chaining together processing components (called actors) that query, transform, analyse, and visualize scientific datasets. Semantic annotations relate data and actor schemas with conceptual information from a shared ontology, to support scientific workflow design, discovery, reuse, and validation in the presence of thousands of potentially useful actors and datasets.

Enabling scientific workflow reuse through structured composition of dataflow and control-flow

Figure for Thought: River Systems are People Systems Too

Figure for Thought: River Systems are People Systems Too

A model for user-oriented data provenance in pipelined scientific workflows

Provenance in collection-oriented workflows

We describe a provenance model tailored to scientific workflows based on the collection-oriented modeling and design paradigm. Our implementation within the Kepler scientific workflow system captures the dependencies of data and collection creation events on preexisting data and collections, and embeds these provenance records within the data stream. A provenance query engine operates on self-contained workflow traces representing serializations of the output data stream for particular workflow runs. We demonstrate this approach in our response to the first provenance challenge.