1. Semantic modeling of System Requirements Lunch presentation @GDMC Lieke Verhelst MSc Student for GIMA Feb 27 th 2009

2. Problem Area Figure taken from: "OGC® Sensor Web Enablement: Overview And High Level Architecture", Mike Botts, George Percivall, Carl Reed, John Davidson Eds. OGC, 2007

3. Examples of ST sensor data Recordings taken from radiosonde and pilot balloon observations, stored in the Integrated Global Radiosonde Archive (IGRA). Records from the Antarctic Iceberg Tracking Database GPS track (Garmin)

4. User questions “How did the frost line move over the years?” “Where and when do icebergs a and b collide?” “Do bikers and hikers get in each other's way in area x?” Combinations of: –Time: instants or intervals historic data or real data –Space: Point, line, region

5. Database implementation Depends on: –Data set (real time, historic data) –User question (spatial analysis? temporal analysis?) –System requirements (speed, storage) –Available solutions (Oracle, DB2, Informix, Postgres/GIS, ESRI) Involves: –Information analysis (data type, data model) –DBA work (indexes, storage)

6. MSc Research Objective: to provide users of sensor data with guidelines how to choose the right database implementation for their purpose. If possible automate this process.

7. Research question Designing a method that models the concepts of sensor data (e.g. sample frequency, size of data file, structure of sensor data) in order to generate a suitable database implementation Apply a weighted qualification to the proposed result Automate the process to the DB implementation

8. How can we model this? UML –Via Classes to tables –Via stereotypes and tagged values to index types –From UML to DDL –…but how to (automatically) guide the user towards selection of the appropriate implementation? -> expert system

9. Semantic modeling IndexSolutionForSpeed <!DOCTYPE rdf:RDF [ ]> <rdf:RDF xmlns:rdf="&rdf;" xmlns:kb="&kb;" xmlns:rdfs="&rdfs;"> <rdfs:Class rdf:about="&kb;Index" rdfs:label="Index"> <rdfs:Class rdf:about="&kb;Speed" rdfs:label="Speed"> <rdf:Property rdf:about="&kb;solutionFor" rdfs:label="solutionFor">

10. OWL extended RDF Constraints, relationships among resources, cardinality, domain and range restrictions, union, disjunction, inverse, transitive OWL–DL based on description logic –existential quantifier , which can be read as “at least one”, or “some” –universal quantifier , which can be read as “only” rules (!!)

11. Research work Steps 1.Create OWL-DL ontology 2.Apply logic & rules 3.Generate DDL 4.Implement in DB

12. Software Used 1.Create OWL-DL ontology (Protégé) 2.Apply logic & rules (Pellet or Racer, Jess, JessTab) 3.Generate DDL (OWL->UML->DDL, Poseidon, Eclipse) 4.Implement in DB

13. DEMOS 2. Demonstrate use of rule engine 1. Demonstrate how reasoner can categorize instance to predefined class