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

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

9. The power of ontologies

10. Experiences and drawbacks  Scope and idiosyncrasies  Language and visualization  Too ahead of the wave?

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

12. Rosa Perez

13. Scope and …: Data puncher  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. D. PuncherOntologist

14. Scope and …: use cases Ontological terms

15. 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.

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