Download GPODS - Wright State University

GPODS
David Luper
Delroy Cameron
Out Line
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General Project Overview
GPS Intro
System Architecture
Data Mining and Semantic Analysis
Example Queries
Future Research
Demo
General Project Overview
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Association Networks
Our Plan of Attack
 Data
Mining
 RDF Metadata
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What we hope to accomplish (analyzing GPS Data)
GPS Intro
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Gives every centimeter on earth a unique address
Latitude Line
Longitude Lines
The Decimal Format System
GPS Intro
System Architecture
Database Schemas
Indexes
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[TargetID], [DateTimeStamp]
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[Latitude], [Longitude]
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Allows for fast retrieval of where everyone in the database was at a
specific time.
[DateTimeStamp], [Latitude], [Longitude]
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Allows for querying the entire database concerning who has ever been
at a specific location.
[DateTimeStamp]
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The primary key allowing for retrieval of a particular person’s
coordinates at a specified slice of time.
Allows for retrieval of who was at a specified location at a specified
time.
[ID], [Latitude], [Longitude]
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Allows for querying all the times a person was at a specific location.
Data Mining and Semantic Analysis
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Binning (Discreet Map)
Association Rule Mining
Data Mining and Semantic Analysis
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GPODS Ontology
Query Processing
 Spatial
 Temporal
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Semantic Associations
GPODS Ontology Schema
gpods:group_name
foaf:Person
gpods:Group
gpods:target_group
rdfs:Literal
subClass_Of
rdfs:Literal
rdfs:Literal
gpods:Target
gpods:right
gpods:left
gpods:target_group_status
gpods:target_name
gpods:Region
rdfs:Literal
rdfs:Literal
gpods:top
gpods:region_name
gpods:target_gender
rdfs:Literal
gpods:bottom
rdfs:Literal
rdfs:Literal
rdfs:Literal
Figure1. GPODS Ontology Schema
Targets Ontology
<gpods:Target rdf:about="Lomez:Iniray">
<gpods:target_name>Iniray Lomez</gpods:target_name>
<gpods:target_gender>F</gpods:target_gender>
<gpods:target_group rdf:resource="#Terror_Group_Green"/>
<gpods:target_group_status>Member</gpods:target_group_status>
</gpods:Target >
<gpods:Target rdf:about="Person:Random198">
<gpods:target_name>Random Person 198</gpods:target_name>
<gpods:target_gender>F</gpods:target_gender>
<gpods:target_group rdf:resource="#Good_Citizen"/>
<gpods:target_group_status>Member</gpods:target_group_status>
</gpods:Target >
Groups Ontology
<gpods:Group rdf:about="#Terror_Cell_Blue">
<gpods:group_name>Terror Cell Blue</gpods:group_name>
</gpods:Group>
<gpods:Group rdf:about="#Terror_Cell_Red">
<gpods:group_name>Terror Cell Red</gpods:group_name>
</gpods:Group>
<gpods:Group rdf:about="#Terror_Cell_Green">
<gpods:group_name>Terror Cell Green
</gpods:group_name>
</gpods:Group>
Query Processing
Target
Group
Region
Time
John Smith
Student
UGA Arch
3/22/2007 12:00pm
John Smith
Student
Terror Cell Green
3/22/2007 3:00pm
John Smith
Student
226 Hardman
3/22/2007 8:00pm
Professor
226 Hardman
3/22/2007 8:00pm
Student
Terror Cell Green
3/22/2007 3:00pm
Jane Doe
Mark Adams
Jane Doe
has_student
John Smith
visited_place
Table1. Semantic Associations in Database
Mark Adams
Spatial Association
gpods:Herndon:Tyler
gpods:Whatley:Amber
gpods:Region277878
gpods:Herndon:Tyler
gpods:Person:Random190
gpods:Region1549
gpods:Region356189
gpods:Crowley:Taylor
Figure5. Spatial Semantic Associations
gpods:Region22986
gpods:Whatley:Amber
Ranking
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Context
Popularity
Association Length
Rarity
Trust
Subsumption
Example Queries
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Simulate an event
Populate Discreet Map
Find places visited by more than 1 person
Export to RDF
Semantic queries for temporal and spatial association
Ranking semantic findings
Association rule mining for probability score
Combining scores for an overall temporal and overall
spatial association score
Future Research
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Path prediction and association trend recognition
Approximate association rule mining and fuzzy
logic
Time sequence neural networks
FOAF integration
Sex offender / child protection queries
Path learning (smart phone meets contextual mapping)
Social networking (Helio)
Demo
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We will show you a demo know prepare
accordingly