Multi-Actor Systems Research Seminar TU Delft February 19, 2010

1. TeLLNet
Reflection Support for
Communities on the Web
Ralf Klamma
RWTH Aachen University
TU Delft, February 19, 2010
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-RK-0210-1

2. I5-RK-0210-2
Prof. Dr. M. Jarke
TeLLNet
Lehrstuhl Informatik 5
(Informationssysteme)
RWTH Aachen
Community Information Systems
Data Management: Mediabases
PALADIN
Case Studies
Agenda
Conclusions and Outlook

3. RWTH Aachen University
• 260 institutes in 9 faculties as Europe’s
leading institutions for science and research
TeLLNet • Currently around 31,400 students are enrolled
in over 100 academic programs
• Over 5,000 of them are international students
hailing from 120 different countries
• 1,250 spin-off businesses have created
around 30,000 jobs in the greater Aachen
region over the past 20 years.
• IDEA League
• Germany’s Excellence Initiative:
3 clusters of excellence, a graduate school
Lehrstuhl Informatik 5 and the institutional strategy “RWTH
Aachen 2020: Meeting Global Challenges”
(Informationssysteme)
Prof. Dr. M. Jarke
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4. Community Information Systems
Research Group
TeLLNet
 Established at DBIS chair, RWTH Aachen University
 9 Phd students & researchers
 10-15 paid student workers & thesis workers
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
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5. Communities of Practice
TeLLNet Analysis of Traces of
CoPs in the Web
Community of
practice (CoP) as the
Engineering of
basic research object
Community
for our Web Science
Information Systems
approach
Communities of
practice are groups of
people who share a
concern or a passion
for something they do
and who interact
regularly to learn how Wenger:
Communities of Practice:
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(Informationssysteme)
to do it better Learning, Meaning and
Prof. Dr. M. Jarke Identity, 1998
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6. i* Model of Requirements
Engineering in CoP
TeLLNet
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(Informationssysteme)
Prof. Dr. M. Jarke
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7. ATLAS: Reflective IS as an
Architectural Foundation
Operational Support Community Reflective Support
Self-Modeling
TeLLNet Can we support CoPs with the Can CoPs continuously elicit and
collaborative creation of complex implement requirements? How much
multimedia objects? computer science support is needed?
Community
Can CoPs make use of metadata over Can CoPs learn meaningful social
the frontiers of media and standards? Self-Observation interaction and make use of
disturbances?
Can we support CoPs by personalized How can CoPs record their complex
knowledge management and networking media learning traces and how they can
strategies in Social Software? deal with them?
How do adaptive, mobile web-based Can CoPs maintain or even improve their
interfaces for CoPs look like? agency (Learning, Researching, Working)
in the Web 2.0?
Actor-
Agent-oriented
Network
RE
Community Theory
Information Systems Social
Participatory Community Game
Network
Design IS Design Theory
Analysis
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Prof. Dr. M. Jarke
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Communities of Practice Media Networks

8. Solution idea for Reflective Support:
Cross-Media Social Network Analysis
 Interdisciplinary multidimensional model of digital networks
TeLLNet
– Social network analysis (SNA) is defining measures for social
relations
– Actor network theory (ANT) is connecting human and media agents
– I* framework is defining strategic goals and dependencies
– Theory of media transcriptions is studying cross-media knowledge
social software Media Networks network of artifacts
Wiki, Blog, Podcast, IM, Chat, Microcontent, Blog entry, Message, Burst, Thread,
Email, Newsgroup, Chat … Comment, Conversation, Feedback (Rating)
i*-Dependencies
(Structural, Cross-media)
network of members
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Members
(Social Network Analysis: Centrality,
(Informationssysteme)
Prof. Dr. M. Jarke
Efficiency)
Communities of practice
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9. Simplified Meta Model
Attribute has Actor
TeLLNet
isA
Medium Artifact Process Member Community
isA
stores creates is affected by belongs go
represents consumes performs ranks
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Browse Address Transcribe … Localize
(Informationssysteme)
Prof. Dr. M. Jarke
Latour: On Recalling ANT, 1999
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10. MediaBase
 Collection of Social Software
artifacts with parameterized
TeLLNet
PERL scripts
– Mailing lists
– Newsletter
– Web sites
– RSS Feeds
– Blogs
 Database support by IBM DB2,
eXist, Oracle, ...
 Web Interface based on Firefox
Plugin, Plone/Zope, LAS, ...
 Strategies of visualization
– Tree maps
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Prof. Dr. M. Jarke
– Cross-media graphs
I5-RK-0210-10 Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006

11. Media Base Web 2.0 Commander
 Personalization (user annotates resources with tags and has his page)
TeLLNet
 Community-awareness (resources and annotation of others are open)
 User-friendly interface (Firefox plug-in, easy insertion of resources, tags, tracking of
recent changes)
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12. Modeling Dependencies
Using the i* Framework
Coordination Iterant
Coordinator
Broker
TeLLNet
isA
isA
isA
Member Gatekeeper Artifact
isA
URL
Hub
Legend:
Agent
Goal
Communication
Network
Resource
Lehrstuhl Informatik 5 Task
(Informationssysteme)
Prof. Dr. M. Jarke
Eric S. K. Yu, Towards Modeling and Reasoning Support for Early-Phase Requirements Engineering, RE 1997
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13. Web 2.0 Media Operations in ATLAS
TeLLNet
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Prof. Dr. M. Jarke
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14. PALADIN: Disturbances in
Cross-media Social Networks
 What is a disturbance?
TeLLNet – Sensing an incompatibility
between theories exposed
and theories-in-use
 Disturbances are starting
points of learning processes
– Disturbances disturb,
prevent … but they are
creating reflection
 Disturbances are hard to
detect or to forecast
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Prof. Dr. M. Jarke
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15. Pattern Language for PALADIN:
Example Troll
Troll Pattern: This pattern tries to discover the cases when a troll exists in a digital social
network. A troll in the network is considered a disturbance.
TeLLNet
Disturbance:
(EXISTS [medium | medium.affordance = threadArtefact]) &
(EXISTS [troll |(EXISTS [thread | (thread.author = troll) &
(COUNT [message | (message.author = troll) &
(message.posted = thread)]) > minPosts]) &
(~EXISTS[ thread1, message1| (thread1.author1 != troll) &
(message1.author = troll & message1.posted = thread1 ]))])])
Forces: medium; troll; network; member; thread; message; url
Force Relations: neighbour(troll, member); own thread(troll, thread)
Solution: No attention must be paid to the discussions started by the troll.
Rationale: The troll needs attention to continue its activities. If no attention is paid, he/she
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will stop participating in the discussions.
Pattern Relations: Associates Spammer pattern.
(Informationssysteme)
Prof. Dr. M. Jarke
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16. Pattern Discovery Process
Pattern 1. Set pattern Pattern Template
parameters Disturbance
Disturbance
TeLLNet
Variables Pattern
4a. Variables Parameters
Change
Pattern Instance Pattern
Parameters
Disturbance Digital Social Network 2. Instantiate
disturbances
4b. Apply
Variables Pattern Pattern Solution
Parameters
Pattern Template Instance
Forces Force
Relations
Disturbance Instances
Description Solution
Variables Pattern
Parameters
Rationale
Dependencies 3. Evaluate
disturbances
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(Informationssysteme) Pattern Relations
Prof. Dr. M. Jarke
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17. PALADIN Case Study
10 patterns of disturbance over 119 social network instances,
TeLLNet 17359 individuals, 215 345 mails
Pattern Occurrences Remarks
Burst 22 The pattern finds out topics which were very important for certain
period of time. Scalability is necessary.
No Conversationalist 76 The existence implies little communication in the network.
No Questioner 67 The existence implies that the network is not popular.
No Answering Person 61 Occurs in small networks. The effects of the lack of an answering
person must be further checked with content analysis.
Troll 2 Troll occurs very rarely in cultural communities. True negatives exist.
Spammer 86 Spammers can be found often in discussion groups. False positives
exist.
Leader 37 The pattern occurs in the network centered around a member.
No Leader 40 Occurs in big networks where the members are distributed in
different clusters.
Structural Hole 67 Occurs for members having neighbors with only one contact.
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(Informationssysteme) Independent 13 Occurs in large networks where disconnected subnetworks exist.
Prof. Dr. M. Jarke
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Discussions Scalability is necessary.

18. Social Network Analysis of
Open Source Communities
 Eclipse components network based on analysis of
TeLLNet source code repository (Software Architecture)
 Eclipse components network based on analysis of
mailing list communication (Social Structure)
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19. Community Reflection about
Development Process
TeLLNet
 Social platform: Eclipse forum eclipsezone
 Forum: Eclipse communication framework (ECF)
 Measure: degree centrality
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 Statistics: 225 nodes, 283 edges
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20. Conversationalist Pattern
 Social platform: Eclipse mailing list
TeLLNet
 Forum: Device debugging developer discussion
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21. Questioner Pattern
 Social platform: Eclipse mailing list
TeLLNet
 Forum: Device debugging developer discussion
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
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22. Correlation Estimation between
Architecture and Social Structure
TeLLNet
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Prof. Dr. M. Jarke
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23. Requirements Reflection Compared
to Community Performance
 With increasing number of boundary spanners it becomes
TeLLNet
easier to induce / implement requirements, which can be
evidenced by increased release rates and vice-versa
 As most bugs are due to insufficient understanding [NOHI99]
and knowledge creation as well as sharing is supported by
boundary spanners [BDBu07], then increased number of
boundary spanners should be evidenced by decreased bug
rate and vice-versa
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24. Identification of End-Users and
Developers in OSS Communities
Community
TeLLNet Clustering
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Prof. Dr. M. Jarke
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25. Textual Analysis of Postings from
Community Experts
TeLLNet
Postings from experts
of one of the identified
communities
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26. Knowledge Network of Computer
Science with AERCS
 Knowledge network to understand major research areas and how they
TeLLNet are interconnected
 Dataset: combination of DBLP and CiteSeerX
- DBLP: 788,259 author’s names, 1,226,412 publications, 3,490 series.
- CiteSeerX: 7,385,652 publications; 22,735,140 references and over 4 million
author’s names
- Matching: 70% publications in DBLP using canopy clustering technique
 Method:
- Citation analysis: bibliographic coupling
- Relatedness measure: cosine similarity
- Series cluster analysis
 Visualization:
Lehrstuhl Informatik 5 - yFiles organic layout (forced-directed paradigm)
(Informationssysteme)
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27. Knowledge Network:
the Visualization
TeLLNet
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28. Interdisciplinary Series:
Top Betweenness Centrality
TeLLNet
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29. High Prestige Series:
Top PageRank
TeLLNet
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30. TeLLNet: SNA for European
Teachers‘ Life Long Learning
TeLLNet Management Analysis Visualization
 How to manage and handle
large scale data on social
networks?
 How to analyse social network
data in order to develop
teachers’ competence, e.g. to
facilitate a better project
collaboration?
 How to make the network
visualization useful for teachers’
lifelong learning?
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
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31. eTwinning
Network Information Visualization
TeLLNet
• Teacher network 2008 as example
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•Cooperation among countries
(Informationssysteme)
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32. Visual Analytics
• Labels and dates help to identify complete substructures
TeLLNet
• substructures
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33. Meta Competence Development
for TeLLNet
TeLLNet
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34. Network Simulation & Evaluation
 Network Simulation
 Teachers profiles (skills, knowledge, identity)
TeLLNet
 Identification of payoffs and strategies
 Network formation
 Network Evaluation
 Nash equilibrium (win-win situation)
 Quality labels
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35. Conclusions
 Can CoPs continuously elicit and implement requirements?
– Eclipse case study
TeLLNet
– EU IP ROLE: RE for personal learning environments
 Can CoPs learn meaningful social interaction and make use of disturbances?
– Pattern-based Cross-Media Network Analysis
– PALADIN case study
 How can CoPs record their complex media learning traces and how they can
deal with them?
– Media Bases
– AERCS case study
 Can CoPs maintain or even improve their agency (Learning, Researching,
Working) in the Web 2.0?
– Measurement, Analysis and Simulation
– TellNet case study
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36. Outlook
 Cloud Data Management for Communities
TeLLNet
– Data uncertainty & security management
– Scaling up analysis in cloud computing
 Mobile Social Software
– Merging Virtual Campfire and spatiotemporal
social network analysis
 Self-Modeling and Self-Observation of Communities
– End-user developement for social network analysis
– Development of meta-competences
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– Lesser need for computer science & IT experts
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