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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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Pusan National University
STEM
Two Viewpoints
Real
World
Representation
of Geographic
Context
Geographic
Context
Application
Systems
Identification How to provide
of Geographic
Geographic
Context
Context ?
How to store and
search Geographic
Context ?
How to analyze
Geographic
Context ?
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STEM
Challenges for Implementation
Context Modeling
Ontology
Representation of Geographic Context
Context Representation
Identification of Geographic Feature
Geo-Labeling
Providing Geographic Context
In-Network Processing
Storing and Searching
Geographic Context
Collecting and
Analyzing Geographic Context
UBGI Middleware
GUID
Standard
Contextual Reasoning and
Context-Aware Mapping
Data Streaming Management
from Geo-Sensors
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Pusan National University
STEM
Context Modeling

Context Modeling



Most basic part of UBGI
A Framework of Context is required to describe context
Context

in Linguistics

in Ubiquitous Computing
Text
Meaning
Context
Fact
Interpretation
Context
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Context as Parameters
Data
Parametric
GML
Interpretation
Contextual
Parameters
User-centric
Meaning
Spatial and Spatiotempoal Context
Behavioral Context
System Environment Context
Human Context
Others
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STEM
Issues of Context Modeling

Classification of Context

Representation of Context


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Spatio-Temporal Properties of Context
Parametric Approach
Ontology and Context
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Pusan National University
STEM
Geo-Labels

Geo-Label: A label for recognizing geographic feature

Implementation

Physical Device

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Virtual Geo-Label

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2-D Bar Code
RFID
Dynamic Computation from Viewpoint
Contents of Geo-Labels

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
UFID
u-Location
Other Information
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Pusan National University
STEM
2-D Bar Codes
Home Page URL,
UFID,
u-Location, and
Other Information
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Virtual Geo-Labels


No Physical Devices
Dynamic Computation of Geo-Labels with 3-D Objects



Position
View Direction
Velocity
Real World
Augmented Reality
on a screen
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Pusan National University
STEM
Implementation of Virtual Geo-Label in 3-D
Geo-Label Mobile Client
Server of 3-D
GIS Databases
Simplification of 3-D
Objects to
Lessen the Computation
Overhead
Server of
Application DB
Position
Progressive Transfer
Dynamic
Computation
Velocity
Interest
View Point
Geo-Label
Presentation of Useful
Information
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Pusan National University
STEM
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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Pusan National University
STEM
Scalability and Real-Time Constraint
Geographic Context
Location DB
stationary and mobile nodes
Dynamic
Updates of
Position
Mobile
Node
Mobile
Node
GIS DB
Context
Request
Mobile
Node
Mobile
Node
Should be processed
in Real-Time
Large Number of Nodes
e.g. 1 Million Nodes
→ 1 sec/ node
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STEM
Geographic Context-Awareness
by In-Network Processing
Scalability Problem
Server
Each node has a small fraction of geographic Information.
Each node exchanges geographic information by
P2P
Sensor Network
Broadcasting
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Pusan National University
STEM
In-Network Processing: Sensor Network
Sensor Network Database
Mobile Ad-Hoc Network (MANET)
No Centralized Server
Multi-Hop
Databases are scattered into mobile node
Needs Geographic Routing
Coverage Area
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In-Network Processing: P2P
Peer-to-Peer
Originally for File Sharing Services
- Examples: Napster, Gnutella, StarCraft
No Centralized Server
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),
(x2,y2,t2),
(x3,y4,t4),
(x4,y4,t4),
IPAddr1
IPAddr2
IPAddr3
IPAddr4
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Data on Air
Data on Air
Broadcasting like DMB
- Needs a Broadcasting Server
- Databases are periodically broadcasted
Hybrid Approach
- Push-Protocol by Broadcasting
- Pull-Protocol by Request on Demand
Broadcasting Server
Broadcasting
Geographic Context
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Pusan National University
STEM
Issues in In-Network Processing: Indexing

Indexing

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
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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Issues in In-Network Processing: Data Format
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Data Format for exchange should be defined
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Data Items to be included in messages
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Distributed Data Structures like distributed index
Efficiency
Heterogeneity
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Standards like SensorML and TransduceML
Middleware for Massively Distributed Systems
Space Heterogeneity
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Issues in In-Network Processing: Protocols
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Distributed Algorithms
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Strongly related with protocol
P2P, Sensor Network, Data on Air, and Hybrid
Example: Data on Air
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Push Protocol
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Tradeoff between data items and period
Determination of Data Items to Broadcast: Hotspot Analysis
Hybrid Approach

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Push Protocol for Hotspot data items
Pull Protocol on demand request
 Other Communication Media like WIBRO
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Pusan National University
STEM
Heterogeneity UBGI Middleware
Client
Client
Client
Massively Distributed
Environment
Middleware
Server
Server
Server
3-Tiers Architecture
Mobile
Node
Mobile
Node
Mobile
Node
Middleware
Middleware
Middleware
Middleware
Middleware
Middleware
Mobile
Node
Mobile
Node
Mobile
Node
Binding Client and Server
Binding Mobile Nodes
Ubiquitous Computing Architecture
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Pusan National University
STEM
Heterogeneity UBGI Middleware
Mobile Node
Mobile Node
Location
Data Server
(GIS)
Performance
Bottleneck
Middleware
Binding Objects
Geographic Binding
LDS
Middleware
Mobile Node
LDS
Standard
e.g. SensorML
Middleware
Mobile Node
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Heterogeneity of
Spaces and Reference Systems
User of UBGI service
Heterogeneous
Representation of Location
(BD218,Room431)
(E121213,N3750015)
(L57,Seg22,49)
Linear Space
Indoor Space
Euclidian Space
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Seamless Space
Linear Space: (L57,Seg22,49)
Indoor Space: (BD218,Room431)
Euclidian Space : (E121213, N3750015)
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Example: Indoor Space

No more Euclidian Space

Different coordinate systems and different properties.
Elevator
Stairs
W.C.
405
401
Emergency Bell A
406
404
4th Floor
p (F4, 401, 15, 18)
Emergency Bell B

We should rebuild Spatial DBMS for Indoor Space
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Pusan National University
STEM
Context-Aware Mapping
user
F
user
G
user
D
user A
Context-Aware
Mapping
user
C
user B
Context-Aware
Mapping
Traditional Map user
user
H
B
user D
user
I
user
A
Context-Aware
Mapping
user C
Context-Aware
Mapping
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Pusan National University
STEM
Context-Aware Mapping
Geographic
Information
For Everyone
My Geographic
Information
Interpretation
My Context
Contextual
Reasoning
My H/W and S/W Context
My Profile
My Status
My Surroundings
Spatial and Spatiotemporal Aspects
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STEM
Context-Aware Mapping: Example
1. Highway or Accessible from Highway
2. Gas stations within 50Km
3. If possible cheapest gas
4. No restaurant for 3 hours
5. GI without complicated visualization
6. GI without heavy geometric computation
Geographic
Features around
My Position
Interpretation
My Context
Small Screen, PDA
Lunch before 30 min.
On a highway
Preference to cheapest gas
Fuel for only 50 Km
Spatial and Spatiotemporal Aspects
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Pusan National University
STEM
Context-Aware Mapping: Requirements

Contextual Reasoning in Real-Time
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Mapping NOT Map itself
Dynamic Context: Data Stream from Geo-Sensors
Two possible approaches

Approach 1: GI with Context-Awareness Features
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Example: Extension of GML with Context-Awareness Tags
More Preprocessing and Less Runtime Contextual Reasoning
Approach 2: GI without Context-Awareness Features
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Example: GML and Agent for Context-Awareness
Less Preprocessing and More Runtime Contextual Reasoning
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Pusan National University
STEM
Data Stream from Geo-Sensors
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Data from sensors: Stream rather than databases
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Data Stream differs from Databases
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Online arrival of data elements, No control over the sequence
Data elements are to be discarded after processed
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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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Summary
Context Modeling
Geo-Labeling
Scalability
In-Network Processing
UBGI Middleware
Heterogeneity
Context-Aware Mapping
Data Streaming Management
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