SlideShare una empresa de Scribd logo
1 de 15
Descargar para leer sin conexión
Delivery
Context
Descrip1ons

  A
Comparison
and
Mapping
Model

                                 Chris&an
Timmerer


               Klagenfurt
University
(UNIKLU)

Faculty
of
Technical
Sciences
(TEWI)

         Department
of
Informa&on
Technology
(ITEC)

Mul&media
Communica&on
(MMC)

h9p://research.1mmerer.com

h9p://blog.1mmerer.com

mailto:chris1an.1mmerer@itec.uni‐klu.ac.at



  Co‐authors:
Chris1an
Timmerer,
Johannes
Jabornig,
and
Hermann
Hellwagner

                                  
(UNIKLU)

Outline

•  Introduc1on
/
Mo1va1on

•  Available
Descrip1on
Formats
+

   Analysis
/
Comparison

•  Mapping
Model
/
Levels
/
Classes

•  Implementa1on

•  Conclusions



2009/03/19
       Chris1an
Timmerer,
Klagenfurt
University,
Austria
   2

Introduc1on

•  Access
to
Internet
is

   ubiquitous

•  Device
types:
sta1onary
+

   mobile

•  Characteris1cs
manifold

•  Calls
for
a
descrip1on
of
the

   usage
environment
context

      –  Different
formats
available


2009/03/19
         Chris1an
Timmerer,
Klagenfurt
University,
Austria
   3

Mo1va1on

  •  Use
case:
mul1media
content
                                                      Terminal
Devices

     adapta1on
    Server
/
Proxy
/

                Live
Content
     Access
Point
/Client

                                  Mul&media
Content

                                     Adapta&on




                                                                                                   Characteris&cs

                Stored
Content
               Adapta&on
                                            Capabili&es

                                            Decision‐Taking
                                        Condi&ons


                                     .
.
.

…
does
not
want
to
change/update
SW
each
&me
a

new
format
appears

…
keep
mapping
effort
minimal

…
want
to
have
a
generic
approach

  2009/03/19
                     Chris1an
Timmerer,
Klagenfurt
University,
Austria
                            4

Available
Descrip1on
Formats

                                                                                      RDF

     Composite
Capabili1es
/
Preference
Profiles
(CC/PP)
–
W3C

• 
      –  Components
+
a9ributes
(simple|complex={bag,seq})

      –  Does
not
define
a
vocabulary
of
terms

     User
Agent
Profile
(UAProf)
–
Open
Mobile
Alliance
(OMA)

• 
           HW/SW
Placorm:
display/audio
output,
interac1on,
media
types,
codecs,
OS,
…

      –                                                                                      CC/PP

           BrowserUA:
(X)HTML
features,
JavaScript,
…

      – 
           NetworkCharacteris1cs:
bearer,
security
op1ons,
Bluetooth
support,
…

      – 
           Wap/PushCharacteris1cs

      – 
     Usage
Environment
Descrip1on
(UED)
–
MPEG‐21
Digital
Item
Adapta1on
(DIA)

• 
      –  User
characteris1cs:
user
informa1on,
preferences,
accessibility,
…
 XML
Schema

      –  Terminal
capabili1es:
display/audio
output,
codecs,
power/storage,
CPU,
…

      –  Network
characteris1cs:
capabili1es/condi1ons,
bandwidth,
error,
…

      –  Natural
environment
characteris1cs:
illumina1on,
noise,
loca1on,
1me,
…


     Delivery
Context
Ontology
(DCO)
–
W3C


• 
                                                              OWL

           Environment:
loca1on,
network,
…


      – 
           Hardware:
display,
input,
memory,
camera,
Bluetooth,
CPU,
…

      – 
           Soiware:
supported
APIs,
data
formats,
OS,
protocols,
Java/Web
browser
specifics,
…

      – 
           Measure:
units
wrt
physical
electrical
charges,
length,
unit
conversion,
…

      – 


2009/03/19
                      Chris1an
Timmerer,
Klagenfurt
University,
Austria
                   5

Analysis
/
Comparison

•  All
standards
make
use
of
XML

      –  MPEG‐21
UED:
XML
Schema

      –  OMA
UAProf:
RDF
(as
it
is
based
on
CC/PP)

      –  W3C
DCO:
OWL

•  Only
a
few
but
essen1al
characteris1cs/capabili1es
are

   common
across
all
usage
environment
context
descrip1on

   formats

      –  Display
capabili1es,
file/coding
formats,
…

      –  Difference
in
syntax,
e.g.,
horizontal=1024,
ver1cal=768
vs.

         1024×768

•  CC/PP
defines
only
a
basic
structure
without
a
vocabulary

   of
terms



describe
rela1onship
between
commonali1es
(how?
which

 technology?)

2009/03/19
             Chris1an
Timmerer,
Klagenfurt
University,
Austria
   6

Mapping
Model

•  Direct
mapping
model:
explicit
func1ons
from
one

   standard/format
to
another

•  Integra1on
model:
common
interface
+
func1on
for

   conver1ng
to/from
this
model

•  Technology

      –  XML
Schema:
data
type
and
value
range
incompa1bili1es

         cannot
be
described
(e.g.,
UED:
colorCapable={true,false},

          UAProf:
ColorCapable={Yes,No})

      –  OWL:
describes
rela1onship
between
classes
and
proper1es

                                         uaprof2dco

         ued2dco

                          dco
                                             integra1on
model

                             dco2uaprof

               dco2ued
                                                im2ued

                                                                                                .
.
.

                                                        ued2im

                     ued2uaprof

              ued
                     uaprof
                                                           dco

                                                                   ued
               uaprof

                     uaprof2ued

2009/03/19
                      Chris1an
Timmerer,
Klagenfurt
University,
Austria
                             7

Approach:
Mapping
Levels

•  Component:
mapping
of
predefined
group
of
elements/a9ributes

   to
similar
group
of
the
other
descrip1on
format

•  Elements:
mapping
of
a9ributes/elements
with
equal
seman1cs
but

   possibly
different
syntax,
i.e.,
different
tag
names

•  Datatype:
mapping
of
datatypes
with
equal
domains
but
different

   syntax


•  Value:
mapping
of
datatypes
with
different
domains
but
equal

   seman1cs



     Level
           UAProf
Example
                                       UED
Example

 Component
 prf:NetworkCharacteristics                              dia:NetworkType
 Element
      prf:InputCharSet                                     dia:CharacterSetCode
 Datatype
     prf-dt:Boolean                                       xsd:Boolean
 Value
        Yes                                                  true

2009/03/19
            Chris1an
Timmerer,
Klagenfurt
University,
Austria
                  8

Examples





2009/03/19
   Chris1an
Timmerer,
Klagenfurt
University,
Austria
   9

Examples
(cont’d)





2009/03/19
    Chris1an
Timmerer,
Klagenfurt
University,
Austria
   10

Mapping
Classes

•  Direct:
equal
seman1cs
and
compa1ble
datatypes
with
equal
domains
but

   may
differ
in
their
syntax
(i.e.,
tag
name)

      –  E.g.,
dia:bitsPerPixel
(xsd:integer)
and
prf:BitsPerPixel
(prf‐dt:Number)


•  Advance:
same
concept
(i.e.,
equal
seman1cs)
but
with
different,
non‐
   compa1ble
datatypes
and/or
domains

      –  E.g.,
dia:Resolu1on
(horizontal/ver1cal
a9ributes)
and
prf:SreenSize
(480x320)


•  Derive:
element
values
can
be
derived
from
one
or
more
elements
of
the

   respec1ve
other
descrip1on
format

      –  E.g.,
prf:SoundOutputCapable
derived
from
presence
of

         dia:AudioOutputCapability


•  Extend:
require
proprietary
extensions
of
the
respec1ve
other
descrip1on

   format

      –  E.g.,
UAProf
WapCharacteristcs
not
present
in
UED

•  UAProf:
77
elements
with
direct
(4),
advance
(7),
derive
(4),
and
extend

   (62)

•  Direct
(4),
advance
(7),
and
derive
(4)
cover
most
mul1media
content

   adapta1on
scenarios

2009/03/19
                  Chris1an
Timmerer,
Klagenfurt
University,
Austria
        11

Example

•  File/coding
format:
Classifica1on
Scheme
vs.
MIME

   type

      –  urn:mpeg:mpeg7:cs:VisualCodingFormatCS:2001:3

      –  video/mp4





2009/03/19
          Chris1an
Timmerer,
Klagenfurt
University,
Austria
   12

Implementa1on





2009/03/19
    Chris1an
Timmerer,
Klagenfurt
University,
Austria
   13

Conclusions

•  Mapping
of
context
delivery
descrip1ons
between
different
formats

•  Mapping
model
based
on
levels
=>
four
classes:
direct,
advance,

   derive,
extend

•  Defined
integra1on
model
+
formulated
templates
(SPARQL/OWL)

   to
query
informa1on
from
this
model
to
generate
the
target
context

   delivery
format

•  Findings

      –  Overlap
between
different
formats
not
that
huge
as
expected

              •  Clustered
around
those
proper1es
which
are
considered
by
the
majority
of

                 applica1ons
areas
(e.g.,
screen
size,
coding
formats,
etc.)

      –  Direct,
advance,
derive
are
sufficient

      –  Rela1onship
described
manually
with
respect
to
an
integra1on
model


              •  Requires
a
thorough
analysis
of
these
formats
which
is
some1mes

                 cumbersome


              •  Mapping
func1ons
need
to
be
defined
only
once


      –  We
have
demonstrated
that
it
is
feasible
but
requires
the
integra1on

         of
many
XML‐based
technologies
(XML
Schema,
RDF,
OWL,
SPARQL,

         XSLT,
…)

2009/03/19
                    Chris1an
Timmerer,
Klagenfurt
University,
Austria
            14

Thank
you
for
your
a9en1on



              ...
ques1ons,
comments,
etc.
are
welcome
…





                                                            
Ass.‐Prof.
Dipl.‐Ing.
Dr.
Chris1an
Timmerer

                                   Klagenfurt
University,
Department
of
Informa1on
Technology
(ITEC)

                                                Universitätsstrasse
65‐67,
A‐9020
Klagenfurt,
AUSTRIA

                                                                  chris1an.1mmerer@itec.uni‐klu.ac.at

                                                                         h9p://research.1mmerer.com/

                                                     Tel:
+43/463/2700
3621
Fax:
+43/463/2700
3699

                                                                                  ©
Copyright:
Chris.an
Timmerer



2009/03/19
              Chris1an
Timmerer,
Klagenfurt
University,
Austria
                                         15


Más contenido relacionado

Similar a Delivery Context Descriptions Comparison and Mapping Model

Roll-out of the NYU HSL Website and Drupal CMS
Roll-out of the NYU HSL Website and Drupal CMSRoll-out of the NYU HSL Website and Drupal CMS
Roll-out of the NYU HSL Website and Drupal CMSChris Evjy
 
Robert Crawford Web Resume
Robert Crawford Web ResumeRobert Crawford Web Resume
Robert Crawford Web Resumerkcrawf
 
The Yahoo Open Stack
The Yahoo Open StackThe Yahoo Open Stack
The Yahoo Open StackMegan Eskey
 
Cloud computing, Virtualisation and the Future
Cloud computing, Virtualisation and the FutureCloud computing, Virtualisation and the Future
Cloud computing, Virtualisation and the FutureAke Edlund
 
Really Simple Document Management - 2009 Update
Really Simple Document Management - 2009 UpdateReally Simple Document Management - 2009 Update
Really Simple Document Management - 2009 UpdateAlfresco Software
 
The Lean Startup at Web 2.0 Expo
The Lean Startup at Web 2.0 ExpoThe Lean Startup at Web 2.0 Expo
The Lean Startup at Web 2.0 ExpoVenture Hacks
 
Fedora App Slide 2009 Hastac
Fedora App Slide 2009 HastacFedora App Slide 2009 Hastac
Fedora App Slide 2009 HastacLoretta Auvil
 
HA+DRBD+Postgres - PostgresWest '08
HA+DRBD+Postgres - PostgresWest '08HA+DRBD+Postgres - PostgresWest '08
HA+DRBD+Postgres - PostgresWest '08Jesse Young
 
geoSDI-Overview-092009
geoSDI-Overview-092009geoSDI-Overview-092009
geoSDI-Overview-092009geoSDI
 
UW ADC - Course 3 - Class 1 - User Stories And Acceptance Testing
UW ADC - Course 3 - Class 1 - User Stories And Acceptance TestingUW ADC - Course 3 - Class 1 - User Stories And Acceptance Testing
UW ADC - Course 3 - Class 1 - User Stories And Acceptance TestingChris Sterling
 
Inside Picnik: How We Built Picnik (and What We Learned Along the Way)
Inside Picnik: How We Built Picnik (and What We Learned Along the Way)Inside Picnik: How We Built Picnik (and What We Learned Along the Way)
Inside Picnik: How We Built Picnik (and What We Learned Along the Way)jjhuff
 
Text Mining and SEASR
Text Mining and SEASRText Mining and SEASR
Text Mining and SEASRLoretta Auvil
 
Building


















 Terrier by
 Open
 Collaboration
Building


















 Terrier by
 Open
 CollaborationBuilding


















 Terrier by
 Open
 Collaboration
Building


















 Terrier by
 Open
 CollaborationCrai Macdonald
 
Hacking Movable Type Training - Day 1
Hacking Movable Type Training - Day 1Hacking Movable Type Training - Day 1
Hacking Movable Type Training - Day 1Byrne Reese
 
Yakov Fain - Design Patterns a Deep Dive
Yakov Fain - Design Patterns a Deep DiveYakov Fain - Design Patterns a Deep Dive
Yakov Fain - Design Patterns a Deep Dive360|Conferences
 

Similar a Delivery Context Descriptions Comparison and Mapping Model (20)

Roll-out of the NYU HSL Website and Drupal CMS
Roll-out of the NYU HSL Website and Drupal CMSRoll-out of the NYU HSL Website and Drupal CMS
Roll-out of the NYU HSL Website and Drupal CMS
 
Robert Crawford Web Resume
Robert Crawford Web ResumeRobert Crawford Web Resume
Robert Crawford Web Resume
 
Mobile Marketing Forum - MOOGA
Mobile Marketing Forum - MOOGAMobile Marketing Forum - MOOGA
Mobile Marketing Forum - MOOGA
 
Cutbots - Presentation
Cutbots - PresentationCutbots - Presentation
Cutbots - Presentation
 
The Yahoo Open Stack
The Yahoo Open StackThe Yahoo Open Stack
The Yahoo Open Stack
 
Cloud computing, Virtualisation and the Future
Cloud computing, Virtualisation and the FutureCloud computing, Virtualisation and the Future
Cloud computing, Virtualisation and the Future
 
Really Simple Document Management - 2009 Update
Really Simple Document Management - 2009 UpdateReally Simple Document Management - 2009 Update
Really Simple Document Management - 2009 Update
 
The Lean Startup at Web 2.0 Expo
The Lean Startup at Web 2.0 ExpoThe Lean Startup at Web 2.0 Expo
The Lean Startup at Web 2.0 Expo
 
Fedora App Slide 2009 Hastac
Fedora App Slide 2009 HastacFedora App Slide 2009 Hastac
Fedora App Slide 2009 Hastac
 
A new King has rise "The mobile phone"
A new King has rise "The mobile phone"A new King has rise "The mobile phone"
A new King has rise "The mobile phone"
 
From Work To Word
From Work To WordFrom Work To Word
From Work To Word
 
HA+DRBD+Postgres - PostgresWest '08
HA+DRBD+Postgres - PostgresWest '08HA+DRBD+Postgres - PostgresWest '08
HA+DRBD+Postgres - PostgresWest '08
 
geoSDI-Overview-092009
geoSDI-Overview-092009geoSDI-Overview-092009
geoSDI-Overview-092009
 
Grails Overview
Grails OverviewGrails Overview
Grails Overview
 
UW ADC - Course 3 - Class 1 - User Stories And Acceptance Testing
UW ADC - Course 3 - Class 1 - User Stories And Acceptance TestingUW ADC - Course 3 - Class 1 - User Stories And Acceptance Testing
UW ADC - Course 3 - Class 1 - User Stories And Acceptance Testing
 
Inside Picnik: How We Built Picnik (and What We Learned Along the Way)
Inside Picnik: How We Built Picnik (and What We Learned Along the Way)Inside Picnik: How We Built Picnik (and What We Learned Along the Way)
Inside Picnik: How We Built Picnik (and What We Learned Along the Way)
 
Text Mining and SEASR
Text Mining and SEASRText Mining and SEASR
Text Mining and SEASR
 
Building


















 Terrier by
 Open
 Collaboration
Building


















 Terrier by
 Open
 CollaborationBuilding


















 Terrier by
 Open
 Collaboration
Building


















 Terrier by
 Open
 Collaboration
 
Hacking Movable Type Training - Day 1
Hacking Movable Type Training - Day 1Hacking Movable Type Training - Day 1
Hacking Movable Type Training - Day 1
 
Yakov Fain - Design Patterns a Deep Dive
Yakov Fain - Design Patterns a Deep DiveYakov Fain - Design Patterns a Deep Dive
Yakov Fain - Design Patterns a Deep Dive
 

Más de Alpen-Adria-Universität

VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesVEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesAlpen-Adria-Universität
 
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingGREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingAlpen-Adria-Universität
 
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Alpen-Adria-Universität
 
VEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionVEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionAlpen-Adria-Universität
 
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingContent-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingAlpen-Adria-Universität
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Alpen-Adria-Universität
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...Alpen-Adria-Universität
 
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...Alpen-Adria-Universität
 
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Alpen-Adria-Universität
 
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Alpen-Adria-Universität
 
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamEvaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamAlpen-Adria-Universität
 
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Alpen-Adria-Universität
 
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingMulti-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingAlpen-Adria-Universität
 
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentPolicy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentAlpen-Adria-Universität
 
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...Alpen-Adria-Universität
 
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesEnergy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesAlpen-Adria-Universität
 
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Alpen-Adria-Universität
 
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningVideo Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningAlpen-Adria-Universität
 
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...Alpen-Adria-Universität
 
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsSARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsAlpen-Adria-Universität
 

Más de Alpen-Adria-Universität (20)

VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesVEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
 
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingGREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video Processing
 
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
 
VEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionVEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission Prediction
 
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingContent-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive Streaming
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
 
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
 
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
 
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
 
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamEvaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
 
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
 
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingMulti-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video Streaming
 
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentPolicy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
 
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
 
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesEnergy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
 
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
 
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningVideo Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
 
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
 
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsSARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
 

Último

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUK Journal
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?Antenna Manufacturer Coco
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 

Último (20)

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 

Delivery Context Descriptions Comparison and Mapping Model

  • 1. Delivery
Context
Descrip1ons
 A
Comparison
and
Mapping
Model
 Chris&an
Timmerer
 Klagenfurt
University
(UNIKLU)

Faculty
of
Technical
Sciences
(TEWI)
 Department
of
Informa&on
Technology
(ITEC)

Mul&media
Communica&on
(MMC)
 h9p://research.1mmerer.com

h9p://blog.1mmerer.com

mailto:chris1an.1mmerer@itec.uni‐klu.ac.at
 Co‐authors:
Chris1an
Timmerer,
Johannes
Jabornig,
and
Hermann
Hellwagner
 
(UNIKLU)

  • 2. Outline
 •  Introduc1on
/
Mo1va1on
 •  Available
Descrip1on
Formats
+
 Analysis
/
Comparison
 •  Mapping
Model
/
Levels
/
Classes
 •  Implementa1on
 •  Conclusions
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 2

  • 3. Introduc1on
 •  Access
to
Internet
is
 ubiquitous
 •  Device
types:
sta1onary
+
 mobile
 •  Characteris1cs
manifold
 •  Calls
for
a
descrip1on
of
the
 usage
environment
context
 –  Different
formats
available
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 3

  • 4. Mo1va1on
 •  Use
case:
mul1media
content
 Terminal
Devices
 adapta1on
 Server
/
Proxy
/
 Live
Content
 Access
Point
/Client
 Mul&media
Content
 Adapta&on
 Characteris&cs
 Stored
Content
 Adapta&on
 Capabili&es
 Decision‐Taking
 Condi&ons
 .
.
.
 …
does
not
want
to
change/update
SW
each
&me
a
 new
format
appears
 …
keep
mapping
effort
minimal
 …
want
to
have
a
generic
approach
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 4

  • 5. Available
Descrip1on
Formats
 RDF
 Composite
Capabili1es
/
Preference
Profiles
(CC/PP)
–
W3C
 •  –  Components
+
a9ributes
(simple|complex={bag,seq})
 –  Does
not
define
a
vocabulary
of
terms
 User
Agent
Profile
(UAProf)
–
Open
Mobile
Alliance
(OMA)
 •  HW/SW
Placorm:
display/audio
output,
interac1on,
media
types,
codecs,
OS,
…
 –  CC/PP
 BrowserUA:
(X)HTML
features,
JavaScript,
…
 –  NetworkCharacteris1cs:
bearer,
security
op1ons,
Bluetooth
support,
…
 –  Wap/PushCharacteris1cs
 –  Usage
Environment
Descrip1on
(UED)
–
MPEG‐21
Digital
Item
Adapta1on
(DIA)
 •  –  User
characteris1cs:
user
informa1on,
preferences,
accessibility,
…
 XML
Schema
 –  Terminal
capabili1es:
display/audio
output,
codecs,
power/storage,
CPU,
…
 –  Network
characteris1cs:
capabili1es/condi1ons,
bandwidth,
error,
…
 –  Natural
environment
characteris1cs:
illumina1on,
noise,
loca1on,
1me,
…

 Delivery
Context
Ontology
(DCO)
–
W3C

 •  OWL
 Environment:
loca1on,
network,
…

 –  Hardware:
display,
input,
memory,
camera,
Bluetooth,
CPU,
…
 –  Soiware:
supported
APIs,
data
formats,
OS,
protocols,
Java/Web
browser
specifics,
…
 –  Measure:
units
wrt
physical
electrical
charges,
length,
unit
conversion,
…
 –  2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 5

  • 6. Analysis
/
Comparison
 •  All
standards
make
use
of
XML
 –  MPEG‐21
UED:
XML
Schema
 –  OMA
UAProf:
RDF
(as
it
is
based
on
CC/PP)
 –  W3C
DCO:
OWL
 •  Only
a
few
but
essen1al
characteris1cs/capabili1es
are
 common
across
all
usage
environment
context
descrip1on
 formats
 –  Display
capabili1es,
file/coding
formats,
…
 –  Difference
in
syntax,
e.g.,
horizontal=1024,
ver1cal=768
vs.
 1024×768
 •  CC/PP
defines
only
a
basic
structure
without
a
vocabulary
 of
terms
 
describe
rela1onship
between
commonali1es
(how?
which
 technology?)
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 6

  • 7. Mapping
Model
 •  Direct
mapping
model:
explicit
func1ons
from
one
 standard/format
to
another
 •  Integra1on
model:
common
interface
+
func1on
for
 conver1ng
to/from
this
model
 •  Technology
 –  XML
Schema:
data
type
and
value
range
incompa1bili1es
 cannot
be
described
(e.g.,
UED:
colorCapable={true,false},
 UAProf:
ColorCapable={Yes,No})
 –  OWL:
describes
rela1onship
between
classes
and
proper1es
 uaprof2dco
 ued2dco
 dco
 integra1on
model
 dco2uaprof
 dco2ued
 im2ued
 .
.
.
 ued2im
 ued2uaprof
 ued
 uaprof
 dco
 ued
 uaprof
 uaprof2ued
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 7

  • 8. Approach:
Mapping
Levels
 •  Component:
mapping
of
predefined
group
of
elements/a9ributes
 to
similar
group
of
the
other
descrip1on
format
 •  Elements:
mapping
of
a9ributes/elements
with
equal
seman1cs
but
 possibly
different
syntax,
i.e.,
different
tag
names
 •  Datatype:
mapping
of
datatypes
with
equal
domains
but
different
 syntax

 •  Value:
mapping
of
datatypes
with
different
domains
but
equal
 seman1cs

 Level
 UAProf
Example
 UED
Example
 Component
 prf:NetworkCharacteristics dia:NetworkType Element
 prf:InputCharSet dia:CharacterSetCode Datatype
 prf-dt:Boolean xsd:Boolean Value
 Yes true 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 8

  • 9. Examples
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 9

  • 10. Examples
(cont’d)
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 10

  • 11. Mapping
Classes
 •  Direct:
equal
seman1cs
and
compa1ble
datatypes
with
equal
domains
but
 may
differ
in
their
syntax
(i.e.,
tag
name)
 –  E.g.,
dia:bitsPerPixel
(xsd:integer)
and
prf:BitsPerPixel
(prf‐dt:Number)

 •  Advance:
same
concept
(i.e.,
equal
seman1cs)
but
with
different,
non‐ compa1ble
datatypes
and/or
domains
 –  E.g.,
dia:Resolu1on
(horizontal/ver1cal
a9ributes)
and
prf:SreenSize
(480x320)

 •  Derive:
element
values
can
be
derived
from
one
or
more
elements
of
the
 respec1ve
other
descrip1on
format
 –  E.g.,
prf:SoundOutputCapable
derived
from
presence
of
 dia:AudioOutputCapability

 •  Extend:
require
proprietary
extensions
of
the
respec1ve
other
descrip1on
 format
 –  E.g.,
UAProf
WapCharacteristcs
not
present
in
UED
 •  UAProf:
77
elements
with
direct
(4),
advance
(7),
derive
(4),
and
extend
 (62)
 •  Direct
(4),
advance
(7),
and
derive
(4)
cover
most
mul1media
content
 adapta1on
scenarios
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 11

  • 12. Example
 •  File/coding
format:
Classifica1on
Scheme
vs.
MIME
 type
 –  urn:mpeg:mpeg7:cs:VisualCodingFormatCS:2001:3
 –  video/mp4
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 12

  • 13. Implementa1on
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 13

  • 14. Conclusions
 •  Mapping
of
context
delivery
descrip1ons
between
different
formats
 •  Mapping
model
based
on
levels
=>
four
classes:
direct,
advance,
 derive,
extend
 •  Defined
integra1on
model
+
formulated
templates
(SPARQL/OWL)
 to
query
informa1on
from
this
model
to
generate
the
target
context
 delivery
format
 •  Findings
 –  Overlap
between
different
formats
not
that
huge
as
expected
 •  Clustered
around
those
proper1es
which
are
considered
by
the
majority
of
 applica1ons
areas
(e.g.,
screen
size,
coding
formats,
etc.)
 –  Direct,
advance,
derive
are
sufficient
 –  Rela1onship
described
manually
with
respect
to
an
integra1on
model

 •  Requires
a
thorough
analysis
of
these
formats
which
is
some1mes
 cumbersome

 •  Mapping
func1ons
need
to
be
defined
only
once

 –  We
have
demonstrated
that
it
is
feasible
but
requires
the
integra1on
 of
many
XML‐based
technologies
(XML
Schema,
RDF,
OWL,
SPARQL,
 XSLT,
…)
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 14

  • 15. Thank
you
for
your
a9en1on
 ...
ques1ons,
comments,
etc.
are
welcome
…
 
Ass.‐Prof.
Dipl.‐Ing.
Dr.
Chris1an
Timmerer
 Klagenfurt
University,
Department
of
Informa1on
Technology
(ITEC)
 Universitätsstrasse
65‐67,
A‐9020
Klagenfurt,
AUSTRIA
 chris1an.1mmerer@itec.uni‐klu.ac.at
 h9p://research.1mmerer.com/
 Tel:
+43/463/2700
3621
Fax:
+43/463/2700
3699
 ©
Copyright:
Chris.an
Timmerer
 2009/03/19
 Chris1an
Timmerer,
Klagenfurt
University,
Austria
 15