Semantic modeling of System Requirements Lunch presentation @GDMC Lieke Verhelst MSc Student for...
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![Page 1: Semantic modeling of System Requirements Lunch presentation @GDMC Lieke Verhelst MSc Student for GIMA Feb 27 th 2009.](https://reader036.fdocuments.in/reader036/viewer/2022062717/56649e2a5503460f94b17cf9/html5/thumbnails/1.jpg)
Semantic modeling of System Requirements
Lunch presentation @GDMC
Lieke Verhelst MSc Student for GIMA
Feb 27th 2009
![Page 2: Semantic modeling of System Requirements Lunch presentation @GDMC Lieke Verhelst MSc Student for GIMA Feb 27 th 2009.](https://reader036.fdocuments.in/reader036/viewer/2022062717/56649e2a5503460f94b17cf9/html5/thumbnails/2.jpg)
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
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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)
#0100120080101002316 56 21100700B 9 10B 31 205 70 10100000 65B 12B 30 205 70 30 95500 -9999 -9999 -9999 215 150
lat: -59.1366 lon: -58.4785 x: 622 y: 2737 file: qush-a-Ant99-202-202.sir backscat: -2.331 contrast: -21.1020 lat: -59.1740 lon: -58.1709 x: 628 y: 2743 file: qush-a-Ant99-203-203.sir backscat: -7.472 contrast: -15.6960 lat: -59.1692 lon: -58.4396 x: 624 y: 2737 file: qush-a-Ant99-204-204.sir backscat: -13.778 contrast: -16.1915
Track Anzere-la Bate-Anzere 10/07/2008 10:05:48 12:14:48 13.2 km 1.1 kph Header Position Time Altitude Depth Leg Length Leg Time Leg Speed Leg Course Trackpoint N46 18 43.6 E7 23 07.0 10/07/2008 10:05:48 2359 m Trackpoint N46 18 45.1 E7 23 08.2 10/07/2008 10:10:25 2359 m 54 m 0:04:37 0.7 kph 28° true Trackpoint N46 18 47.1 E7 23 11.7 10/07/2008 10:13:26 2343 m 97 m 0:03:01 2 kph 51° true Trackpoint N46 18 49.8 E7 23 14.0 10/07/2008 10:14:43 2337 m 97 m 0:01:17 5 kph 30° true
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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
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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)
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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.
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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
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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
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Semantic modeling
Index SolutionFor Speed
<?xml version='1.0' encoding='UTF-8'?><!DOCTYPE rdf:RDF [
<!ENTITY rdf 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'> <!ENTITY kb 'http://protege.stanford.edu/kb#'> <!ENTITY rdfs 'http://www.w3.org/2000/01/rdf-schema#'>
]><rdf:RDF xmlns:rdf="&rdf;"
xmlns:kb="&kb;" xmlns:rdfs="&rdfs;">
<rdfs:Class rdf:about="&kb;Index" rdfs:label="Index"><rdfs:subClassOf rdf:resource="&rdfs;Resource"/>
</rdfs:Class><rdfs:Class rdf:about="&kb;Speed"
rdfs:label="Speed"><rdfs:subClassOf rdf:resource="&rdfs;Resource"/>
</rdfs:Class><rdf:Property rdf:about="&kb;solutionFor"
rdfs:label="solutionFor"><rdfs:domain rdf:resource="&kb;Index"/><rdfs:range rdf:resource="&rdfs;Class"/>
</rdf:Property></rdf:RDF>
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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 (!!)
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Research work Steps
1. Create OWL-DL ontology
2. Apply logic & rules
3. Generate DDL
4. Implement in DB
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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
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DEMOS
2. Demonstrate use of rule engine
1. Demonstrate how reasoner can categorize instance to predefined class