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DDMS AND IRMA

DDMS AND IRMA. Experiences and Drawbacks. Overview. Quick view at DDMS and IRMA. The use of ontologies within our projects. The benefits of using them. Suggestion that might be useful for decision support systems. Players. Individuals. Organizations. Funders Management Domain experts

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DDMS AND IRMA

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  1. DDMS AND IRMA Experiences and Drawbacks

  2. Overview • Quick view at DDMS and IRMA. • The use of ontologies within our projects. • The benefits of using them. • Suggestion that might be useful for decision support systems.

  3. Players Individuals Organizations Funders Management Domain experts Translation group Developers Users Stakeholders CSU UADY LSTM MRC IVCC (BMGF) NIH, MoHs Google, Qualcom, Bayer

  4. DDMS • What is the DDMS? • Designed around the control of vector borne diseases • Target users • Multi level. data puncher – decision maker • Developmental stage • Version 3 • Goal • Multi disease, country wide implementations

  5. IRMA • What is IRMA? • Designed around the needs of a laboratory running routine insecticide resistance work. • Target users • Scientists, laboratory technician • Developmental stage • Alpha, tested by just a few. MIRO before BFO. • Goal • Recording day to day activities of a laboratory.

  6. The engine that powers DDMS THE NEXT-GENERATION APPLICATION FRAMEWORK BY

  7. METADATA is an application blueprint • Automatically generate code • Decrease development time • Make changes with less effort

  8. The power of ontologies

  9. Experiences and drawbacks • Scope and idiosyncrasies • Language and visualization • Too ahead of the wave?

  10. Decision Support Systems Data Data Data Program Data Entry Data Data Analysis Display Strategy Collection Storage Retrieval Data Management tool , XHTML files , & Sampling format , SQL data Manage - GIS software , Text files , Methodology schemes Data entry warehouse ment tool Statistical packages , GIS software , screens Modeling Google Earth , Outputs : Charts , Graphs , Maps , Tables FEEDBACK TO Interpretation PROGRAM STRATEGY & ( comparison with local historical data , relation to critical thresholds etc ) METHODOLOGY Management Decisions Scope and idiosyncrasies Our computersystems are here WHO State gov. IDO-Mal D. Puncher

  11. Rosa Perez

  12. Scope and …: Data puncher D. Puncher Ontologist • 0-low DOM expertise. • Just needs terms. • I know what I need now. • High DOM expertise. • Can create terms. • I know what I know and I think I know what you will need.

  13. Scope and …: use cases Ontological terms

  14. Language and visualization • Guashinton vs. Washington • translations to local character sets. • Schadenfreude. • Ontologies are graphs not trees • Most users have experience with a tree control.

  15. Too ahead of the wave? Ontology • OO very common. • Talking about “semantics” is esoteric. • WHO has to be a player in the. ?

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