Representing Uncertainty, Profile and Movement History in 不确定性,轮廓与运动史

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Obtained Results
Deviation variation by Time Unit
Deviation
Deviation X Time Unit
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4 3
2 1
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Eduardo Nóbrega, Valeria Times and
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Obtained Results
Speed Variation by Time Unit
Speed X Time Unit
Speed
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4
3
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1
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Conclusions
The major problem of mobile objects database is to deal with the continuous movements.
To deals with the object movements the ADR was used
Eduardo Nóbrega, Valeria Times and
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Obtained Results
Arco Verde-Pesqueira Route
Eduardo Nóbrega, Valeria Times and
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Obtained Results
Comparison between the predicted distance and the real distance.
Distance
Predicted distance X Real distance
600 500 400 300 200 100
For the developed application, the obtained predicted distances were very similar to the real distances
Eduardo Nóbrega, Valeria Times and
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Obtained Results
Movement Simulator
Eduardo Nóbrega, Valeria Times and
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Obtained Results
An example of a input file given to the movement simulator :
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Basic Concepts
Eduardo Nóbrega, Valeria Times and
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Related Work
… …………Models Features
Most
Bey Yi
CHOROCHRON OS
MOMENT
Vazirgianni s
Bear d
Eduardo Nóbrega, Valeria Times and
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Introduction
Advance of Technology
Portable devices
Display resolution, troughput, storage, reduction of the dimensions, weight and energy consumption.
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Future Work
Add spatial operators to the proposed data model
Add the profile and movement history to the prototype to improve the movement prediction
Sist la
Nóbre ga
Spatial-Temporal database
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Use of movement Profile
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Deal with uncertainty
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Use of dynamic attributes
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Mobile regions aspects
Server Module
Location prediction and stores the movement profile of all mobile objects;
Eduardo Nóbrega, Valeria Times and
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The Data Model
Position prediction is done based on history movement
Modeling of trajectory uncertainty is done based on deviation calculation
A prototype has been implemented:
Applications of Geographic Information System involving time (e.g. control of regions evolutions).
Eduardo Nóbrega, Valeria Times and
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Introduction
Examples of Mobile applications:
Real time applications sensible to location (e.g. Fleet management, tracking of trains and aircrafts, ships monitoring);
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The End
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Eduardo Nóbrega, Valeria Times and
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The Data Model
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The Data Model
Eduardo Nóbrega, Valeria Times and
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Introduction
Location Based Retrieval
Continuous Movement Distrubuted data
Updates
Eduardo Nóbrega, Valeria Times and
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Motivation
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Obtained Results
Uncertainty Variation by Time Unit
Uncertainty
Uncertainty X Time Unit
6
5432 Nhomakorabea1
0
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Time Unit
Eduardo Nóbrega, Valeria Times and
<Position X>, <Position Y>, <Band width>, <Availability>, <Processor usage>, <Time for the next reading>
1: 275,270,256,133,60,1100 2: 276,270,256,133,60,1700 3: 277,270,256,133,60,3200 4: 278,270,256,133,60,1500 5: 279,270,256,133,65,2900 6: 280,272,256,133,62,1500
Eduardo Nóbrega, Valeria Times and
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The Data Model
Eduardo Nóbrega, Valeria Times and
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The Data Model
Basic Classes
0 0
Real Distance Predicted Distance
200 400 600
Time Unit
800 1000
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Contributions
A conceptual data model for moving objects has been developed:
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Basic Concepts
Movement profiles Dynamic attributes Deviation Uncertainty
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Define basic spatial operators
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The Data Model
Is divided in two modules:
Client Module
Deals with the location capture, calculates the uncertainty and the deviation, and makes the location prediction;
Wireless networks
Bandwith, Infrastructure more efficient (cellular nets – GSM and CDMA, satellite communication).
Eduardo Nóbrega, Valeria Times and
Research have been done to solve the mobile objects problems.
Continuous updates Data model design Spatial operators
Eduardo Nóbrega, Valeria Times and
Representing Uncertainty, Profile and Movement History in Mobile Objects Databases
Authors: Eduardo Nóbrega Valeria Times José Rolim
Schedule
Introduction Basic Concepts Related Work The Data model Obtained Results Conclusions Contributions Future Work