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Ubiquitous GIS Part III: Implementation Issues
Fall 2007
Ki-Joune Li
http://isel.cs.pnu.edu/~lik
Pusan National University
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STEMPusan National University
STEM-PNU
Two Viewpoints
GeographicContext
RealWorld
ApplicationSystems
How to provideGeographicContext ?
How to store andsearch Geographic
Context ?
How to analyzeGeographicContext ?
Representation of Geographic
Context
Identification of Geographic
Context
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STEMPusan National University
STEM-PNU
Challenges for Implementation
Representation of Geographic Context
Identification of Geographic Feature
Providing Geographic Context
Storing and Searching Geographic Context
Collecting and Analyzing Geographic Context
Context Modeling
Context Representation
Ontology
Geo-Labeling GUID
In-Network Processing
UBGI Middleware Standard
Contextual Reasoning and Context-Aware Mapping
Data Streaming Managementfrom Geo-Sensors
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STEMPusan National University
STEM-PNU Context Modeling
Most basic part of UBGI A Framework of Context is required to describe context
Context in Linguistics
in Ubiquitous Computing
Context Modeling
Text Meaning
Context
Fact Interpretation
Context
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STEMPusan National University
STEM-PNU
Context as Parameters
Data Interpretation
Spatial and Spatiotempoal Context
Behavioral Context
System Environment Context
Human Context
Others
ParametricGML
ContextualParameters
User-centricMeaning
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STEMPusan National University
STEM-PNU
Issues of Context Modeling
Classification of Context
Representation of Context Spatio-Temporal Properties of Context Parametric Approach
Ontology and Context
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STEMPusan National University
STEM-PNU
Geo-Labels
Geo-Label: A label for recognizing geographic feature
Implementation Physical Device
2-D Bar Code RFID
Virtual Geo-Label Dynamic Computation from Viewpoint
Contents of Geo-Labels UFID u-Location Other Information
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STEMPusan National University
STEM-PNU
2-D Bar Codes
Home Page URL,UFID,u-Location, andOther Information
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STEMPusan National University
STEM-PNU
Virtual Geo-Labels
No Physical Devices Dynamic Computation of Geo-Labels with 3-D Objects
Position View Direction Velocity
Real World
Augmented Realityon a screen
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STEMPusan National University
STEM-PNU
Implementation of Virtual Geo-Label in 3-D
Server of 3-DGIS Databases
Server ofApplication DB
Geo-Label Mobile Client
Position
Velocity
Interest
View Point
Geo-Label
DynamicComputation
Presentation of UsefulInformation
Progressive Transfer
Simplification of 3-D Objects to
Lessen the Computation Overhead
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STEMPusan National University
STEM-PNU
Issues of Geo-Label
Implementation of Virtual Geo-Labels iPointer TM of IST Paper Map Panoramic View of 3-D objects
Storing GUID in Geo-Label GUID: Global Unique Identifier
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STEMPusan National University
STEM-PNUShould be processed
in Real-Time
Large Number of Nodese.g. 1 Million Nodes
→ 1 sec/ node
Scalability and Real-Time Constraint
Geographic Context
MobileNode
MobileNode
DynamicUpdates of
Position
ContextRequest
MobileNode
MobileNode
MobileNode
MobileNode
MobileNode
MobileNode
GIS DBGIS DBLocation DBstationary and mobile nodes
Location DBstationary and mobile nodes
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STEMPusan National University
STEM-PNUServer
Geographic Context-Awareness by In-Network Processing
Scalability Problem
Each node has a small fraction of geographic Information.
Each node exchanges geographic information by
P2P Sensor Network Broadcasting
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STEMPusan National University
STEM-PNU
In-Network Processing: Sensor Network
Sensor Network DatabaseSensor Network Database
No Centralized ServerMobile Ad-Hoc Network (MANET)
Databases are scattered into mobile node
Coverage Area
Multi-Hop
Needs Geographic Routing
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STEMPusan National University
STEM-PNU
In-Network Processing: P2P
Peer-to-Peer
No Centralized ServerOriginally for File Sharing Services - Examples: Napster, Gnutella, StarCraft
Sensor Network or Infrastructure Network - Each node has an IPv6 address - No Geographic Limit unlike sensor network
Databases are scattered into mobile nodes
(x1,y1,t1), IPAddr1(x2,y2,t2), IPAddr2(x3,y4,t4), IPAddr3(x4,y4,t4), IPAddr4
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STEMPusan National University
STEM-PNU
Data on Air
Data on AirData on Air
Broadcasting like DMB - Needs a Broadcasting Server - Databases are periodically broadcasted
BroadcastingGeographic Context
Broadcasting Server
Hybrid Approach - Push-Protocol by Broadcasting - Pull-Protocol by Request on Demand
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STEMPusan National University
STEM-PNU
Issues in In-Network Processing: Indexing
Indexing Databases are scattered into small pieces at local devices NO GLOBAL Server storing a Global Index Modification of
DHT (Distributed Hash Table) or Distributed Index Structures
are required
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STEMPusan National University
STEM-PNU
Issues in In-Network Processing: Data Format
Data Format for exchange should be defined Data Items to be included in messages
Distributed Data Structures like distributed index Efficiency Heterogeneity
Standards like SensorML and TransduceML Middleware for Massively Distributed Systems Space Heterogeneity
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STEMPusan National University
STEM-PNU
Issues in In-Network Processing: Protocols
Distributed Algorithms Strongly related with protocol P2P, Sensor Network, Data on Air, and Hybrid
Example: Data on Air Push Protocol
Tradeoff between data items and period Determination of Data Items to Broadcast: Hotspot Analysis Hybrid Approach
Push Protocol for Hotspot data items Pull Protocol on demand request
Other Communication Media like WIBRO
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STEMPusan National University
STEM-PNU
Ubiquitous Computing Architecture
Heterogeneity UBGI Middleware
MobileNode
MobileNode
MobileNode
Middleware Middleware Middleware
MobileNode
MobileNode
MobileNode
Middleware Middleware Middleware3-Tiers Architecture
Server Server Server
Middleware
Client Client Client
Massively Distributed Environment
Binding Client and Server
Binding Mobile Nodes
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STEMPusan National University
STEM-PNUPerformanceBottleneck
Heterogeneity UBGI Middleware
Middleware
Binding ObjectsGeographic Binding
Location Data Server
(GIS)
Mobile Node Mobile Node
Middleware Middleware
Mobile Node Mobile Node
LDS LDSStandard
e.g. SensorML
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STEMPusan National University
STEM-PNU
Heterogeneity of Spaces and Reference Systems
Linear Space
Euclidian Space
(L57,Seg22,49) (E121213,N3750015)
Indoor Space
(BD218,Room431)
Heterogeneous Representation of Location
User of UBGI service
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STEMPusan National University
STEM-PNU
Seamless Space
Linear Space: (L57,Seg22,49)
Indoor Space: (BD218,Room431) Euclidian Space : (E121213, N3750015)
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STEMPusan National University
STEM-PNU
Example: Indoor Space
No more Euclidian Space Different coordinate systems and different properties.
We should rebuild Spatial DBMS for Indoor Space
Emergency Bell A
401
W.C.
404
405
406
ElevatorStairs
Emergency Bell Bp (F4, 401, 15, 18)
4th Floor
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STEMPusan National University
STEM-PNU
Context-Aware Mapping
Traditional Map
userA
userB
userI
userD
userC
userF
userG
userH
Context-AwareMapping
user A Context-AwareMapping
user B
Context-AwareMapping
user C
Context-AwareMapping
user D
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STEMPusan National University
STEM-PNU
Spatial and Spatiotemporal Aspects
Context-Aware Mapping
GeographicInformation
For Everyone
My GeographicInformation
My Context
My Profile
My Status
Interpretation
Contextual Reasoning
My Surroundings
My H/W and S/W Context
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STEMPusan National University
STEM-PNU
Context-Aware Mapping: Example
Spatial and Spatiotemporal Aspects
GeographicFeatures around
My Position
1. Highway or Accessible from Highway2. Gas stations within 50Km3. If possible cheapest gas4. No restaurant for 3 hours5. GI without complicated visualization6. GI without heavy geometric computation
My Context
Lunch before 30 min.
On a highway
Interpretation
Preference to cheapest gas
Small Screen, PDA
Fuel for only 50 Km
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STEMPusan National University
STEM-PNU
Context-Aware Mapping: Requirements
Contextual Reasoning in Real-Time Mapping NOT Map itself Dynamic Context: Data Stream from Geo-Sensors
Two possible approaches Approach 1: GI with Context-Awareness Features
Example: Extension of GML with Context-Awareness Tags More Preprocessing and Less Runtime Contextual Reasoning
Approach 2: GI without Context-Awareness Features Example: GML and Agent for Context-Awareness Less Preprocessing and More Runtime Contextual Reasoning
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STEMPusan National University
STEM-PNU
Data Stream from Geo-Sensors
Data from sensors: Stream rather than databases
Data Stream differs from Databases Online arrival of data elements, No control over the sequence Data elements are to be discarded after processed
Only small size of memory to store them Continuous queries rather than “one-time” query
DSMS: Different Approaches from conventional DBMS Query Processing, Indexing etc.. Stream Mining rather than Data Mining
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STEMPusan National University
STEM-PNU
Summary
Context Modeling
Heterogeneity
Geo-Labeling
Scalability In-Network Processing
UBGI Middleware
Context-Aware Mapping
Data Streaming Management