1 / 18

XMDR

XMDR. Presentation for FDAS. eXtended Metadata Registry. Sam Chance chances@saic.com. 13 September 2007. Project Overview. Project Background. Collaborative, Interagency Effort DOD, EPA, LBNL, USGS, NCI, Mayo Clinic…Others?

heather-cox
Download Presentation

XMDR

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. XMDR Presentation for FDAS eXtended Metadata Registry Sam Chance chances@saic.com 13 September 2007 Project Overview

  2. Project Background • Collaborative, Interagency Effort • DOD, EPA, LBNL, USGS, NCI, Mayo Clinic…Others? • Draws on and Contributes to Interagency Cooperation on Ecoinformatics • Involves International, National, State, Local Government Agencies, other Organizations • Recognizes Great Potential of Semantics-based Computing, Management of Metadata • Improving Collection, Maintenance, Dissemination, Processing of Very Diverse Data Structures • Collaboration Arises from Need to ShareDiverse Data Across Multiple Organizations • Project Initiated July 2004 www.xmdr.org

  3. Improve data management through use of stronger semantics management Databases XML data Other “traditional” data Enable new wave of semantic computing Take meaning of data into account Process across relations as well as properties May use reasoning engines, e.g., to draw inferences MDR Semantic ManagementProposals for 11179 Edition 3 From XMDR Project

  4. The Ageless Information Problemcf: Data, Information, Knowledge, Wisdom Getting the information that we need, when we need it, without afflicting the excellent minds of humans with toil and drudgery The litany: • Too much or too little, irrelevant, not authoritative, out of date • Unknown quality, not trustable, lacks provenance, no certainty measures • Difficult to find, difficult to access, difficult to use • Meaning not clear, relationship to other information not clear • Data creators do not have the same understanding of the data as end users • Recorded data loses much real world meaning, context, relationships • Much of the meaning of data is buried in the processes used to manipulate the data (e.g., in computer code) • Need improvements in efficiency and effectiveness From XMDR Project

  5. New Semantics Capabilities for ISO/IEC 11179 MDR (Edition 3) Proposed by XMDR project • Improve traditional data management/data administration • Use stronger semantics management and semantics services capabilities • Enable something new • Semantic computing From XMDR Project

  6. Questions about Semantics • Questions that are swirling around emerging semantics technologies • Can these improve traditional data management/Data Administration? • Do these open new doors? Answer new questions? • How does this fit into the rest of what my organization is doing? • How can my organization use these new technologies? • Why and how should my organization invest in the infrastructure that is necessary to make effective use of semantic technologies? • How is my organization aligning? What is the strategy? From XMDR Project

  7. Semantic Computing: The Nub of It • Processing that takes “meaning” into account • Makes use of concept systems, e.g., thesauri and/or ontologies • Moves some of the “meaning” of data from computer code to managed semantics • Processing that uses (e.g., reasons across) the relations between things not just computing about the things themselves. • Processing that helps to take people out of the computation, reducing the human toil • Semantics “grounding” for data, data discovery, extraction, mapping, translation, formatting, validation, inferencing, … • Delivering higher-level results that are more helpful for the user’s thought and action From XMDR Project

  8. In The Epic Information StruggleWe Have Made Heroic Progress Files Computer Processing Cards Tape Disk Machine Processing From XMDR Project

  9. In The Epic Information StruggleWe Have Made Heroic Progress In documenting data and text (e.g., semantics management) – • Data Standards • Code sets • (Meta)Data Standards • Data element definitions, valid values, value meanings • Metadata registries (MDR, ISO/IEC 11179) • Other standards as presented at this conference • Concept systems (or KOS) • Glossaries • Dictionaries • Thesauri • Taxonomies • Ontologies • Graphs In structuring data and text -- • Structured Data • Columns on cards & tape (possibly comma separated) • Hierarchical (DBMS) • Network • Table (relational DBMS) • Hierarchy (XML) • Graph (RDF) • Semi-structured text • Nrof, trof, LaTeX … • SGML • HTML • XML From XMDR Project

  10. US DoD’s Daunting Metadata Challenges Courtesy of ghayes@mitre.org

  11. Integration Problem…N x N Model of Inefficiency and Ineffectiveness

  12. XML Management Challenge XML: One Language, Many Vocabularies <latitude_degrees>30N</latitude_degrees> <latitude units=“degrees” hemisphere=“north”>30</latitude> <lat> <hemisphere>N</hemisphere> <deg>30</deg> </lat> • These 3 XML Fragments are: • Equally Valid Ways to Express the Same Data in XML • Well-formed per W3C XML Specification • Mediation Required for Interoperability From XMDR Project Courtesy of ghayes@mitre.org

  13. Semantics in SOA Standards Based Design (OSGi, SCA, W3C) S S O A SemanticsBasedComputing DistributedComputing Autonomy Adaptability Dynamic Discovery Event Driven Monitor & Manage Recovery Oriented Knowledge Mgmt Up/Down Scale Knowledge Representation Services /Agents Events /Messaging Ontology Proof /Trust DynamicNetworks DistributedTransactions Logic

  14. The Meta-* Factor SPR SSR SMDR Heterogeneity • Logical (s/w) • Physical (h/w) • S3 MDR Abstraction,Location Independence ProtocolAgnostic From Global Infotek, Inc.

  15. Challenge: Use data from systems that record the same facts with different terms Software Component Registries Dublin Core Registries Common Content Common Content Database Catalogs Common Content ISO 11179Registries UDDIRegistries Table Column Data Element Common Content Common Content Business Specification Country Identifier OASIS/ebXMLRegistries CASE Tool Repositories XML Tag Attribute Common Content Common Content Business Object Coverage TermHierarchy OntologicalRegistries Common Content From XMDR Project

  16. Same Fact, Different Terms Name: Country Identifiers Context: Definition: Unique ID: 5769 Conceptual Domain: Maintenance Org.: Steward: Classification: Registration Authority: Others DataElementConcept Algeria Belgium China Denmark Egypt France . . . Zimbabwe Data Elements Algeria Belgium China Denmark Egypt France . . . Zimbabwe L`Algérie Belgique Chine Danemark Egypte La France . . . Zimbabwe DZ BE CN DK EG FR . . . ZW DZA BEL CHN DNK EGY FRA . . . ZWE 012 056 156 208 818 250 . . . 716 Name: Context: Definition: Unique ID: 4572 Value Domain: Maintenance Org. Steward: Classification: Registration Authority: Others ISO 3166 3-Alpha Code ISO 3166 English Name ISO 3166 French Name ISO 3166 2-Alpha Code ISO 3166 3-Numeric Code From XMDR Project

  17. Challenge: Draw information together from a broad range of studies, databases, reports, etc. From XMDR Project

  18. Information Extraction & Semantic Computing Extraction Engine Discover patterns Select models Fit params Inference Report results Segment Classify Associate Normalize De-duplicate 11179-3 (E3) XMDR Actionable Information Decision Support From XMDR Project

More Related