Talk given at the 5th Internation Conference on Communities and Technologies (Workshop C: Making Sense of Twitter). Thanks to Axel Bruns and Jean Burgess who organized a great session!
SmartSociety – A Platform for Collaborative People-Machine ComputationHong-Linh Truong
We present the SmartSociety Platform for Collaborative People-Machine computation carried out in the FET SmartSociety project: http://www.smart-society-project.eu/
Talk given at the 5th Internation Conference on Communities and Technologies (Workshop C: Making Sense of Twitter). Thanks to Axel Bruns and Jean Burgess who organized a great session!
SmartSociety – A Platform for Collaborative People-Machine ComputationHong-Linh Truong
We present the SmartSociety Platform for Collaborative People-Machine computation carried out in the FET SmartSociety project: http://www.smart-society-project.eu/
fsdfgList of Course Work Subjects
S.NO SEM SUBJECT CODE SUBJECT TITLE ELECTIVE/CORE CREDIT
1 1 22MC202 MACHINE LEARNING
TECHNIQUES CORE 3
2 1 22PRM01
RESEARCH METHODOLOGY AND
IPR CORE 3
3 1 22MC302
ADVANCED ARTIFICIAL
INTELLIGENCE ELECTIVE 3
4 3 22MC209 ADVANCED INTERNET OF THINGS CORE 3
5 3
22PVD30 SYSTEM LEVEL HARDWARE SOFTWARE CODESIGN ELECTIVE 3
6 3 22MC324
INFORMATION RETRIEVAL
TECHNIQUES ELECTIVE 3
22MC202 MACHINE LEARNING TECHNIQUES
Course Objective 1. To introduce students to the basic concepts and techniques of Machine Learning.
2. To have a thorough understanding of the Supervised and Unsupervised learning techniques
3. To implement linear and non-linear learning models
4. To implement distance-based clustering techniques
5. To understand graphical models of machine learning algorithms
Unit I FUNDAMENTALS OF MACHINE LEARNING 9
Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis – Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear regression.
Unit II LINEAR MODELS 9
Multi-layer Perceptron – Going Forwards – Going Backwards: Back Propagation Error – Multi-layer Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back-Propagation – Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality – Interpolations and Basis Functions – Support Vector Machines
Unit III DISTANCE-BASED MODELS 9
Nearest neighbor models – K-means – clustering around medoids – silhouettes – hierarchical clustering
– Density based methods- Grid based methods- Advanced cluster analysis- k-d trees – locality sensitive hashing – non-parametric regression – bagging and random forests – boosting – meta learning
Unit IV
TREE AND RULE MODELS
9
Decision trees – learning decision trees – ranking and probability estimation trees – regression trees
– clustering trees – learning ordered rule lists – learning unordered rule lists – descriptive rule
learning – Mining Frequent patterns, Association and Correlations, advanced association rule techniques-first order rule learning
Unit V
REINFORCEMENT LEARNING AND GRAPHICAL MODELS
9
Reinforcement Learning – Overview – Getting Lost Example – Markov Decision Process, Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution – Markov Chain Monte Carlo – Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models –
Tracking Methods.
TOTAL HOURS: 45 PERIODS
CO1 Understanding distinguish between, supervised, unsupervised and semi- supervised learning
CO2 Apply the appropriate machine learning strategy for any given problem
Course Outcome
CO3 Suggestion of using supervised, unsupervised or semi-superv
Towards Enterprise Interoperability Service UtilitiesBrian Elvesæter
B. Elvesæter, F. Taglino, E. D. Grosso, G. Benguria and A. Capellini, “Towards Enterprise Interoperability Service Utilities”, paper presentation at IWEI 2008, Munich Germany, 18 September 2008.
Presentation of main traits of the present and future AT ecosystem in Europe. The importance of a interoperable accessibility API is pointed out and the need of an European centralised portal of initiative to develop a more dynamic market is introduced.
Our research aims to propose a global approach for specification, design and verification of context awareness Human Computer Interface (HCI). This is a Model Based Design approach (MBD). This methodology describes the ubiquitous environment by ontologies. OWL is the standard used for this purpose. The specification and modeling of Human-Computer Interaction are based on Petri nets (PN). This raises the question of representation of Petri nets with XML. We use for this purpose, the standard of modeling PNML. In this paper, we propose an extension of this standard for specification, generation and verification of HCI. This extension is a methodological approach for the construction of PNML with Petri nets. The design principle uses the concept of composition of elementary structures of Petri nets as PNML Modular. The objective is to obtain a valid interface through verification of properties of elementary Petri nets represented with PNML.
30th IEEE International Conference onAdvanced Information Networking and Applications (AINA-2016) March 23-25, 2016, Crans-Montana, Switzerland
Connected Smart Cities: Interoperability with SEG 3.0 for the Internet of Things
Semantic Interoperability
Methodology
Linked Open Data
Linked Open Vocabularies
Linked Open Reasoning
Linked Open Services
Internet of Things
Web of Things
Semantic Web of Things
Smart cities
Presentation made for the event "Digital transformation in France and Germany: Consequences for industry, society & higher education" organized by the French-German University in cooperation with Institut Mines-Télécom https://www.dfh-ufa.org/fr/digital-transformation-in-france-and-germany/
Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive In...Universita della Calabria,
Doctoral Defence in ICT (Università della Calabria, Italy). Ph.D. candidate Claudio Savaglio. Thesis title: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems.
Project number: 247765
Project acronym: VERITAS
Project full title: Virtual and Augmented Environments and Realistic User Interactions To achieve Embedded Accessibility DesignS
Starting date: 1 January 2010
Duration: 48 Months
VERITAS is an Integrated Project (IP) within the 7th Framework Programme, Theme FP7-ICT-2009.7.2, Accessible and Assistive ICT
http://veritas-project.eu/
I-ESA 2010, The International Conference on Interoperability for Enterprise S...Le Scienze Web News
I-ESA 2010, The International Conference on Interoperability for Enterprise Software and Applications
COVENTRY, United Kingdom
Doctoral Symposium: April 12th, 2010
Workshop Day: April 13th, 2010
Conference: April 14th – 15th, 2010
A Web Services Infrastructure for the management of Mashup InterfacesApplied Computing Group
"A Web Services Infrastructure for the management of Mashup Interfaces" J. Vallecillos, J. Criado, A.J. Fernández-García, N. Padilla and L. Iribarne.
Applied Computing Group, University of Almería, Spain
11th International Workshop on Engineering Service-Oriented Applications (WESOA’2015) Goa, India, November 26th 2015
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fsdfgList of Course Work Subjects
S.NO SEM SUBJECT CODE SUBJECT TITLE ELECTIVE/CORE CREDIT
1 1 22MC202 MACHINE LEARNING
TECHNIQUES CORE 3
2 1 22PRM01
RESEARCH METHODOLOGY AND
IPR CORE 3
3 1 22MC302
ADVANCED ARTIFICIAL
INTELLIGENCE ELECTIVE 3
4 3 22MC209 ADVANCED INTERNET OF THINGS CORE 3
5 3
22PVD30 SYSTEM LEVEL HARDWARE SOFTWARE CODESIGN ELECTIVE 3
6 3 22MC324
INFORMATION RETRIEVAL
TECHNIQUES ELECTIVE 3
22MC202 MACHINE LEARNING TECHNIQUES
Course Objective 1. To introduce students to the basic concepts and techniques of Machine Learning.
2. To have a thorough understanding of the Supervised and Unsupervised learning techniques
3. To implement linear and non-linear learning models
4. To implement distance-based clustering techniques
5. To understand graphical models of machine learning algorithms
Unit I FUNDAMENTALS OF MACHINE LEARNING 9
Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis – Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear regression.
Unit II LINEAR MODELS 9
Multi-layer Perceptron – Going Forwards – Going Backwards: Back Propagation Error – Multi-layer Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back-Propagation – Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality – Interpolations and Basis Functions – Support Vector Machines
Unit III DISTANCE-BASED MODELS 9
Nearest neighbor models – K-means – clustering around medoids – silhouettes – hierarchical clustering
– Density based methods- Grid based methods- Advanced cluster analysis- k-d trees – locality sensitive hashing – non-parametric regression – bagging and random forests – boosting – meta learning
Unit IV
TREE AND RULE MODELS
9
Decision trees – learning decision trees – ranking and probability estimation trees – regression trees
– clustering trees – learning ordered rule lists – learning unordered rule lists – descriptive rule
learning – Mining Frequent patterns, Association and Correlations, advanced association rule techniques-first order rule learning
Unit V
REINFORCEMENT LEARNING AND GRAPHICAL MODELS
9
Reinforcement Learning – Overview – Getting Lost Example – Markov Decision Process, Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution – Markov Chain Monte Carlo – Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models –
Tracking Methods.
TOTAL HOURS: 45 PERIODS
CO1 Understanding distinguish between, supervised, unsupervised and semi- supervised learning
CO2 Apply the appropriate machine learning strategy for any given problem
Course Outcome
CO3 Suggestion of using supervised, unsupervised or semi-superv
Towards Enterprise Interoperability Service UtilitiesBrian Elvesæter
B. Elvesæter, F. Taglino, E. D. Grosso, G. Benguria and A. Capellini, “Towards Enterprise Interoperability Service Utilities”, paper presentation at IWEI 2008, Munich Germany, 18 September 2008.
Presentation of main traits of the present and future AT ecosystem in Europe. The importance of a interoperable accessibility API is pointed out and the need of an European centralised portal of initiative to develop a more dynamic market is introduced.
Our research aims to propose a global approach for specification, design and verification of context awareness Human Computer Interface (HCI). This is a Model Based Design approach (MBD). This methodology describes the ubiquitous environment by ontologies. OWL is the standard used for this purpose. The specification and modeling of Human-Computer Interaction are based on Petri nets (PN). This raises the question of representation of Petri nets with XML. We use for this purpose, the standard of modeling PNML. In this paper, we propose an extension of this standard for specification, generation and verification of HCI. This extension is a methodological approach for the construction of PNML with Petri nets. The design principle uses the concept of composition of elementary structures of Petri nets as PNML Modular. The objective is to obtain a valid interface through verification of properties of elementary Petri nets represented with PNML.
30th IEEE International Conference onAdvanced Information Networking and Applications (AINA-2016) March 23-25, 2016, Crans-Montana, Switzerland
Connected Smart Cities: Interoperability with SEG 3.0 for the Internet of Things
Semantic Interoperability
Methodology
Linked Open Data
Linked Open Vocabularies
Linked Open Reasoning
Linked Open Services
Internet of Things
Web of Things
Semantic Web of Things
Smart cities
Presentation made for the event "Digital transformation in France and Germany: Consequences for industry, society & higher education" organized by the French-German University in cooperation with Institut Mines-Télécom https://www.dfh-ufa.org/fr/digital-transformation-in-france-and-germany/
Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive In...Universita della Calabria,
Doctoral Defence in ICT (Università della Calabria, Italy). Ph.D. candidate Claudio Savaglio. Thesis title: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems.
Project number: 247765
Project acronym: VERITAS
Project full title: Virtual and Augmented Environments and Realistic User Interactions To achieve Embedded Accessibility DesignS
Starting date: 1 January 2010
Duration: 48 Months
VERITAS is an Integrated Project (IP) within the 7th Framework Programme, Theme FP7-ICT-2009.7.2, Accessible and Assistive ICT
http://veritas-project.eu/
I-ESA 2010, The International Conference on Interoperability for Enterprise S...Le Scienze Web News
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A Web Services Infrastructure for the management of Mashup InterfacesApplied Computing Group
"A Web Services Infrastructure for the management of Mashup Interfaces" J. Vallecillos, J. Criado, A.J. Fernández-García, N. Padilla and L. Iribarne.
Applied Computing Group, University of Almería, Spain
11th International Workshop on Engineering Service-Oriented Applications (WESOA’2015) Goa, India, November 26th 2015
Cruzando el abismo educativo de la ingeniería de software utilizando Software...Applied Computing Group
`Cruzando el abismo educativo de la ingenieria de software utilizando Software como Servicio y computación en nube'
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fox@cs.berkeley.edu
JISBD'2012 (XVII Jornadas de Ingeniería del Software y Bases de Datos)
Jornadas SISTEDES 2012 (17 a 19 septiembre de 2012)
Universidad de Almería
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
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An interaction meta-model for cooperative component-based user interfaces
1. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
1
An Interaction Meta-model for
Cooperative Component-Based
User Interfaces
L. Iribarne(1), N. Padilla(1), J. Criado(1), C. Vicente-Chicote(2)
(2) Information Technology and Communications
Technical University of Cartagena, Spain
cristina.vicente@upct.es
(1) Applied Computing Group (TIC-211)
University of Almeria, Spain
{liribarne, npadilla, javi.criado}@ual.es
ISDE2010 – OTM Workshops 26 October 2010, Crete (Greece)
2. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
2
1. Motivation
2. Interaction Metamodel
3. Case Study
4. Future work
CONTENT
3. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
3
1. Motivation
2. Interaction Metamodel
3. Case Study
4. Future work
CONTENT
4. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
4
Knowledge Society
Collaborative
Information
Systems
<<require>>
social interaction
(WIS) Web-based Infomation System
1. Motivation
<<globalization>>
<<instance>>
User profiles
(decision-making)
CSCW
Subsystem Subsystem
Adaptable
user-interface
Adaptable
user-interface
<<social interaction>>
<<cooperate>>
<<interact>> <<interact>>
<<cooperate>>
“subsystems will probably require self-adaptable user interfaces.”
5. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
5
(WIS)
Environmental Management Information Systems (EMIS)
<<example>>
1. Motivation
SOLERES System “our implementation”
“There is a huge interaction
between groups of people for
environmental management.”
<<instance>>
Knowledge Society
Collaborative
Information
Systems
<<require>>
social interaction
<<globalization>>
Politicians,
Technicians,
Administrators,
…
<<roles>>
6. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
6
Environmental
Management
Information
System
Neural-Networks
Knowledge
Environmental Info
Ontologies
Cooperative Systems
Multi-Agents
Satellite Images
Cellular-Automata clasif.
Component-based systems
Trading agents
SOLERES
“application, integration and development of multidisciplinary works”
SOLERES Framework
1. Motivation
R&D (TIN2007-61497)
Spanish Ministry of Science and Innovation
7. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
7 SOLERES Project
Correlation
cartography
satellite
Env. map
CA-based Clasif.
Neural-Net
Agents,Trading,Ontologies
1. Motivation
User Interfaces
self-adapatable
8. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
8 SOLERES Project
Correlation?
cartography
satellite
Env. map
AC-based Clasif.
Neural-Net
Agents,Trading,Ontologies
1. Motivation
SCOPE OF THE WORK self-adapatable
9. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
9
Main Goal:
An MDE-based methodology for evolutive (adaptable) User Interfaces
Solution:
1. Model-Driven Engineering (MDE) solution.
2. Model Evolution (by considering UI as models).
3. Model transformation & Trading services.
Considerations:
1. Component-based user interfaces.
2. COTS (commercial off-the-shelf) UI components.
3. WIMP simple interfaces (Windows, Icons, Menus and Pointers).
4. Web-based User Interfaces as supporting of WCIS (at runtime).
1. Motivation
11. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
11
CUI1 CUI2 CUI3
CUI4 CUI5
CUI6
CUI7
“COTS-component” architecture
1. Motivation
“cotsget” component
COTS + get (widget/gadget-type)
Considerations:
1. Component-based UI.
2. COTS UI components.
3. WIMP simple interfaces
4. Web-based UI
<<has>>
CUIi
...
Functional
Interaction
Visual
Temporal
templates
Cotsget repositories
“public repositories generally managed by trading servicies.”
<<has>>
dependency-issues
COTSGETS
(commercial components)
[Iribarne et al., 2004]
12. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
12
CUI1 CUI2 CIU3
CUI4 CUI5
CUI6 CUI7
A B C
t
CUI1
CUI4
CUI6 CUI7
A B C
CUI8
2:Regeneration
1: Transformation
UIe
1: Transformation
2:Regeneration
CUI1 CUI9 CUI3
CUI4
A B C
cooperation &
interaction issues
1. Motivation
Goal: self-adaptable interfaces
“instances of the user interface when social interaction and cooperation issues occur.”
cooperation &
interaction issues
cooperation &
interaction issues
13. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
13
CUI1 CUI2 CUI3
CUI4 CUI5
CUI6 CUI7
A B C
CUI1 CUI9 CUI3
CUI4
A B C
t
mt
Model A Model B
Rmt
Trading
RT
Transformation
rules
Trading
RT
User interface A User interface B
Model Transformation
(MDE)
1. Motivation
Regeneration
Regeneration
M2M
Metamodel
“For our purposes, we consider
the user-interface as a model.”
I V
S
14. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
14
CUI1 CUI2 CUI3
CUI4 CUI5
CUI6 CUI7
A B C
CUI1 CUI9 CUI3
CUI4
A B C
t
mt
Model A Model B
Rmt
Trading
RT
Transformation
rules
Trading
RT
User interface A User interface B
Model Transformation
(MDE)
1. Motivation
Regeneration
Regeneration
M2M
Metamodel
“For our purposes, we consider
the user-interface as a model.”
Interaction +
I V
S
15. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
15
1. Motivation
2. Interaction Metamodel
3. Case Study
4. Future work
CONTENT
16. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
16
UIA UIB
2. Interaction Metamodel
“The methodology considers
interface evolution.”
Architectural Metamodel
Architectural Modeli
Architectural Metamodel
Architectural Modeli
17. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
17
Architectural Metamodel
Architectural Modeli
2. Interaction Metamodel
Architectural Metamodel
1
2 3
1. Structural metamodel:
composition dependencies
between components through
connection ports (i.e., provided
and required interfaces).
2. Visual metamodel: components
behaviour from a visual point
of view (open, close, show,
hide components, etc.) by
means of a state machine.
3. Interaction metamodel: models
the user-interaction behaviour,
and describes the structure of
interaction tasks that users may
complete in the system.
18. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
18
Architectural Metamodel
Architectural Modeli
2. Interaction Metamodel
Architectural Metamodel
1
2 3
1. Structural metamodel:
composition dependencies
between components through
connection ports (i.e., provided
and required interfaces).
2. Visual metamodel: components
behaviour from a visual point
of view (open, close, show,
hide components, etc.) by
means of a state machine.
3. Interaction metamodel: models
the user-interaction behaviour,
and describes the structure of
interaction tasks that users may
complete in the system.
19. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
19
2. Interaction Metamodel
Architectural Metamodel
Architectural Modeli
20. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
20
2. Interaction Metamodel
main concepts
Architectural Metamodel
Architectural Modeli
21. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
21
2. Interaction Metamodel
<<shared elements>>
Architectural Metamodel
Architectural Modeli
<<interconnection>>
22. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
22
2. Interaction Metamodel
Roles:
<<politician>>
<<technician>>
<<administrator>>
Group:
Actor:
Actor:
Actor:
Cotsget:
Cotsget:
Cotsget:
Architectural Metamodel
Architectural Modeli
23. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
23
2. Interaction Metamodel
Architectural Metamodel
Architectural Modeli
<<atomic task>>
24. APPLIED COMPUTING GROUP
UNIVERSITY OF ALMERIA (SPAIN)
ISDE’2010 – OTM Workshops
26th October 2010, Crete (Greece)
Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
24
AND
OR
complexsimple
2. Interaction Metamodel
“A choreography describes the
protocol, or the sequence in which
the tasks must be executed.”
Activity-diagram
Architectural Metamodel
Architectural Modeli
“To facilitate the modelling,
concepts have been collected
in an ‘enumeration-class’ in
the MM.”
25. APPLIED COMPUTING GROUP
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Aninteractionmeta-modelforcooperativecomponent-baseduserinterfaces
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2. Interaction Metamodel
Activity-Diagram
r1
r2
r3
r1
r2
In order to complete the semantical closure of the
interaction metamodel:
r3
…
Architectural Metamodel
Architectural Modeli
incoming (1)
outgoing (2..*)
26. APPLIED COMPUTING GROUP
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1. Motivation
2. Interaction Metamodel
3. Case Study
4. Future work
CONTENT
27. APPLIED COMPUTING GROUP
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3. A Case Study
Scenario: EMIS cooperative task for decision-making on natural disasters.
<<politician>>
<<GISexpert>>
<<evaluator>>
UI1
UI2
UI3
Analyzes land areas in order
to classify types of soil,
affected area, etc.
Carries out an economic
study from that information
provided by the GIS expert.
Wishes to carry out a
particular assessment of a
natural disaster.
Cooperative task
requesting information
“Environmental study”
“Economic study”
28. APPLIED COMPUTING GROUP
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3. A Case Study“cooperative-interaction diagram (model)”
“adapted activity-diagram notation”
29. APPLIED COMPUTING GROUP
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3. A Case Study
Metamodel
Model
30. APPLIED COMPUTING GROUP
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3. A Case Study
31. APPLIED COMPUTING GROUP
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3. A Case Study
<<InitialStep>>
<<FinalStep>>
Main coreographies
32. APPLIED COMPUTING GROUP
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3. A Case Study
<<InitialStep>>
<<FinalStep>>
#1/3
#3/3
#6/3#7/3
#5/1
#4/1
#2/1
<<simple>> <<complex>>
1 2 3
[ #n/n = #step/replaced-by ]
33. APPLIED COMPUTING GROUP
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3. A Case Study
#1/3
#3/3
#6/3#7/3
#4.2/2
#5/1
#4/1
#2/1
#4.1/3
#4.3/1
#4.4/1
#4.5/3
#5.2/2
#5.1/3
#5.3/1
#5.4/1
#5.5/3
<<simple>> <<complex>>
1 2 3
[ #n/n = #step/replaced-by ]
“a similar behaviour (step #4) occurs in step #5”
34. APPLIED COMPUTING GROUP
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3. A Case Study
#1/3
#2/1
#3/1
#4/1
#5/3
<<simple>> <<complex>>
1 2 3
“both expert and evaluator have also their own choreography”
#1/3
#2/1
#3/1
#4/1
#5/3
[ #n/n = #step/replaced-by ]
Environmental
study
Economic
study
“a similar behaviour has the choreography associated to the evaluator rol”
35. APPLIED COMPUTING GROUP
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1. Motivation
2. Interaction Metamodel
3. Case Study
4. Future work
CONTENT
36. APPLIED COMPUTING GROUP
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a) Automated co-evolution of models [Cicchetti et al., 2008]
b) Metamodel adaptation techniques [Wachsmuth, 2007]
c) Model Evolution concepts [Blair et al., 2009]
4. Future Work
Future Work:
1. GUI tool (Eclipse GMF) to draw new scenarios (models).
2. Simulation tool (to reproduce events and interactions; groups/UI).
3. Variability (chage-detection) of Interaction Metamodel:
37. APPLIED COMPUTING GROUP
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An Interaction Meta-model for
Cooperative Component-Based
User Interfaces
L. Iribarne, N. Padilla, J. Criado and C. Vicente-Chicote
ISDE2010 – OTM Workshops 26 October 2010, Crete (Greece)
More info: http://www.ual.es/acg/soleres
Contact: luis.iribarne@ual.es Thanks !!
SOLERES R&D Project
TIN2007-61497
Applied Computing Group
Ref. TIC-211
Engineering Higher
Polytechnic School
University of Almeria
Campus, Spain
Spanish Ministry of
Science and Innovation
38. APPLIED COMPUTING GROUP
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Appendix
Complementary
Slides
39. APPLIED COMPUTING GROUP
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Implementation Framework (MDE)
Eclipse Modeling Project, EMP, http://www.eclipse.org/modeling/
EMF (Eclipse Modeling Framework), “.ecore” diagrams
http://www.eclipse.org/modeling/emf/
ATL (Atlas Transformation Language)
http://www.eclipse.org/m2m/atl/
OCL (Object Constraint Language), OMG
GMF (Graphical Modeling Framework)
http://www.eclipse.org/modeling/gmp/
Visual Paradigm SDE for Eclipse, http://www.visual-paradigm.com/
UML scenarios as models of the metamodel
Appendix. Implementation Details
40. APPLIED COMPUTING GROUP
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Appendix. Implementation Details
41. APPLIED COMPUTING GROUP
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Appendix. Implementation Details
42. APPLIED COMPUTING GROUP
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Appendix. Implementation Details
43. APPLIED COMPUTING GROUP
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Metamodel
Model
<<Graphical View>> <<Object View>>
<<Views>>
“metamodel-checkings” “model transformation”
Appendix. Implementation Details
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Appendix. Implementation Details
An example of a COTGET-based user interface
Structural model view
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CUI1 CUI2 CUI3
CUI4 CUI5
CUI6 CUI7
A B C
CUI1 CUI9 CUI3
CUI4
A B C
t
mt
Model A Model B
Rmt
Trading
RT
Transformation
rules
Trading
RT
User interface A User interface B
Model Transformation
(MDE)
Regeneration
Regeneration
M2M
Metamodel
Intelligent Model-Transformation
a) Deterministic transformation
b) Hybrid transformation
c) Intelligent transformation
I V
S
Appendix. Implementation Details
50. APPLIED COMPUTING GROUP
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repository
Push model
trader
Pull model
(bots or search engines)
ServiceFetcher
export
import
“Trader Federation”
“Trader Interfaces”
“Trader Structure”
“Trader Invocations”
Appendix. Implementation DetailsTRADING SERVICE
OMG-ODP
Trading