National Center for Ecological Analysis and Synthesis

Search

Search Results

5551-5560 of 6337
  1. Publication

    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.

    Access Product View Project »
  2. Publication

    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.

    Access Product View Project »
  3. Publication

    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.

    Access Product View Project »
  4. Publication

    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.

    Access Product View Project »
  5. Publication

    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.

    Access Product View Project »
  6. Publication

    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.

    Access Product View Project »
  7. Publication

    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.

    Access Product View Project »
  8. Publication

    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.

    Access Product View Project »
  9. Publication

    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.

    View Project »
  10. Publication

    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.

    Access Product View Project »