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Incremental validation of xml documents

Accessing and using sensor data within the Kepler scientific workflow system

Incorporating semantics in scientific workflow authoring

The tools used to analyze scientific data are often distinct from those used to archive, retrieve, and query data. A scientific workflow environment, however, allows one to seamlessly combine these functions within the same application. This increase in capability is accompanied by an increase in complexity, especially in workflow tools like Kepler, which target multiple science domains including ecology, geology, oceanography, physics, and biology.

Improving data discovery in metadata repositories through semantic search

The amount of ecological data available electronically is increasing at a rapid rate, e.g., over 15,000 data sets are available today in the Knowledge Network for Biocomplexity (KNB) alone. Using the existing search capabilities of these online data repositories, however, scientists struggle to quickly locate data that are relevant to their needs or that will integrate with their current data sets. Semantic technologies aim at addressing many of these problems and hold the promise of enabling more powerful "smart" searches of online data archives.

Report on the EDBT'2002 panel on scientific data integration

Various issues related to scientific data integration are discussed. The goal of data integration is to construct a global description, called global schema, of the data coming from a multitude of heterogeneous sources. Data integration systems generally follow a semantic approach to integration based on the conceptual schemas or metadata of the sources to be integrated and on a middleware data model for a uniform and semantically rich representation of heterogeneous sources.

Incremental navigation: Providing simple and generic access to heterogeneous structures

We present an approach to support incremental navigation of structured information, where the structure is introduced by the data model and schema (if present) of a data source. Simple browsing through data values and their connections is an effective way for a user or an automated system to access and explore information. We use our previously defined Uni-Level Description (ULD) to represent an information source explicitly by capturing the source’s data model, schema (if present), and data values.

Using the uni-level description (uld) to support data-model interoperability

We describe a framework called the Uni-Level Description (ULD) for accurately representing information from a broad range of data models. The ULD extends previous meta-data-model approaches by: (a) providing uniform representation and access to data model, schema, and data, and (b) supporting data models with non-traditional schema arrangements, including those that allow optional and multiple levels of schema.

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.