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Semantic Integration of Heterogeneous NASA Mission Data Sources

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Semantic Integration of Heterogeneous NASA Mission Data Sources

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    1. Semantic Integration of Heterogeneous NASA Mission Data Sources

    2. Goal: Virtual data integration Virtual Integration: enable construction of single virtual data source that presents a semantically unified view across a set of heterogeneous data sources

    3. Outline Semantic Integration Basics SemanticIntegrator (SI) Architecture Application to Science Ops Planning Conclusions

    4. What is Information Integration? Integration involves: bringing together information from multiple sources synthesizing a unified view of the information

    5. Syntactic vs. Semantic Integration Syntactic integration: integrate based on surface commonalities across data labels and values Eg: correspond “temperature” field in one DB with “temperature” field in another based on identical field name and datatype (numeric) Semantic integration : integrate based on commonalities in meaning behind data Eg: correspond “temperature” fields based on the fact that both measure the same property of the same physical subsystem and their scientific units are compatible

    6. Shallow vs. Deep Integration Shallow integration: retrieve the union of all potentially-relevant information from all data sources and present everything to the user (Google-like approach) Deep integration: synthesize a single view from all available data sources and present that integrated view to the user Requires defining a common integrated view of data Requires identification and disambiguation of similar data across sources Is challenging!

    7. Deep, Semantic Integration Example

    8. Ontologies: Key to Deep, Semantic Integration

    9. Outline

    10. Generic Integration Architecture

    11. Integration Problems/Approaches

    12. SemanticIntegrator Architecture

    13. Outline

    15. Mobile Agents Data Sources Field Data Images of field sites, environ. features, mineral samples Voice notes Site data (lat/lon, topography) Stored in ScienceOrganizer semantic repository Analytic Data Sample analysis data (e.g., composition) Stored in Excel spreadsheet Mineralogy Data Chemical composition, atomic weight Available @ minerals.com GIS Data Satellite images Geographic data (e.g., population, features) Available from Microsoft’s TerraServer Web service

    16. Applying SemanticIntegrator to Mobile Agents 4 data sources 5 ontologies: 4 different ontologies to impart meaning to data sources 1 ontology represents the integrated source Rules capture translations between sources Simple interface to display integrated data (SIMA: SemanticIntegrator for Mobile Agents)

    17. Ontologies

    18. SIMA Interface

    19. Outline

    20. Conclusions Goal: avoid expensive “hard-coded”, “one-off” integration strategies SemanticIntegrator’s explicit integration framework enables reuse of components and knowledge, reducing incremental integration overhead: Data source wrappers/ontologies can be reused Rules can be reused

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