The slides of the invited talk Maurizio Marchese from the LiquidPub team gave at the Workhop on Automated Experimentation at e-Science Institute, Edinburgh, February 24th, 2010

1. Automated Experimentation
in Social Informatics
Maurizio Marchese
Department of Information Engineering and Computer Science - DISI
University of Trento, Italy
Workshop on Automated Experimentation, February 23, 2010, Edinburgh

2. Early work (ca. 1990)
G. Jacucci, M. Marchese and C. Uhrik, "Composing Simulations by expert rules: modeling plasma
sprayed films", in "Knowledge Based Hybrid Systems in Engineering and Manufacturing", edited by I.
Mezgar and P. Bertok, , Elsevier Science Publisher B.V. , 1993, North-Holland

3. Early work (ca. 1990)
G. Jacucci, M. Marchese and C. Uhrik, "Composing Simulations by expert rules: modeling plasma
sprayed films", in "Knowledge Based Hybrid Systems in Engineering and Manufacturing", edited by I.
Mezgar and P. Bertok, , Elsevier Science Publisher B.V. , 1993, North-Holland
Porosity 4%
Porosity 10%
Porosity 17%

4. Early work (ca. 1990)

5. What we were missing
• Computational power
• Appropriate level of abstraction
• Appropriate specification languages

6. Social Informatics
• Social Informatics (SI) refers - among others -
to the body of research and study that examines
the uses of information technologies in social
contexts
• Two examples:
▫ P2P systems in eResponse domain
▫ Discovery of Scientific Communities

7. Use case 1:
Automated Experimentation in eResponse

8. Automated Experimentation in
eResponse
• In emergency contexts…
▫ Large number of actors
involved
▫ Geographically dispersed
agents collaborate and
coordinate
▫ “Live” experimentation is
difficult and expensive

9. Automated Experimentation in
eResponse
• Automated Experimentation
▫ Enable to explore different
architectures for information
sharing
▫ Enable to explore dynamic
and flexible interaction
patterns
• OpenKnowledge use-case
▫ Development of a
simulation environment
through which different
information gathering
strategies are modelled and
simulated

10. • An interaction-driven mechanism relying on a
distributed infrastructure (OK Kernel);
• Enable finding and coordination of peers by
publishing, discovering and executing
interaction models (IM), i.e. multi party
conversational protocols, specified in
Lightweight Coordination Calculus (LCC)

11. A Flood Case Study
Prealarm sensor network
• An Emergency Monitoring
System (EMS):
▫ gathers data from sensors
placed in the town
▫ checks weather information
in order to enrich the data
needed to predict the
evolution of a potential flood
▫ sends an alarm to the
emergency coordinator when
the situation becomes critical

12. Poll-reporter LCC
interaction model

13. A Flood Case Study: sensor network

14. A Flood Case Study
Evacuation
• Agents (e.g., emergency subordinates such fire-fighters)
move to specific locations assigned by the coordinator

15. The e-Response System Architecture

16. Experiments Configuration
Scenario
Exp N° Runs Variable Settings
Description
- Number of Moving Peers =1
1 Centralized 20
- Paths = 1 x run
- Flooding Law = fixed
- Topology Nodes = 60 x run
2 Decentralized 20 - Number of Reporter peers (x
node) = 1

17. Experimental Results

18. Automated Experimentation
benefits
• Explore diverse parameters in complex
environments
• Inject fault conditions (e.g., disruption of
communication channels and inaccurate signaling)
• Test the conditions where a p2p architecture
improves the overall performance and robustness
over traditional centralized architectures
• Test whether an specific IT platform (OK Kernel)
can supports the coordination of team-members in
an emergency site (e.g., reporters as mobile agents)

19. Use case 2:
Scientific Community Discovery

20. Scientific Community discovery:
• In research, researchers write contributions together,
they publish their advances in some event or journal.
• Their contributions refer other contributions, some
contributions are organized in collections, and so on.
• They create a big network with interesting relations, and
a community structure that could be used (among
others) to improve two main aspect in the research
scope: search and assessment.
▫ search for a contribution, or group them; people working in
similar content, events that are related to a contribution,
▫ measure the impact on a specific community (normalize the
actual metrics), narrow down the search space into a
community structure, and so on.

21. Community Detection Process 1
1 (one author in common)
Conf A Conf B
Conf A Conf B
Community A Community B
Conference Network
1 5 1 5
2 4 2 4
3 6 3 6
Overlapping Between Communities of Common Authors
%

22. Overview of the DBLP Network
SOFTWARE ENGINEERING(kbse,icse)
DIST. SYSTEM/COMPILER(ipps,iccS) APPLIED COMPUTING/CRYPTO(sac,compsac)
TELETEACHING/HUM_INT (chi,hicss)
TELECOM (icc,globecom)
HUMMAN –COMP INTER(icchp,hci)
GENETIC AND EVO ALG(cec,gecco)
AI/DB (icai,aaai)
ROBOTIC/M.MEDIA (icra,icpr)

23. Normalizing Metrics
h-index Citations
38.6 6676.6
24.6 25 25.2
21.2 20.4 3587
18.8
2846 3008
14.25 13
2055.2 1989.4
1332.8
749.5 579.8

24. Community Detection Process 2
Citation Network Complete Network
C2 C3 Citation U Authorship U Affiliation
C1
C4 P1 P2
Authorship C5
Network
C2
O1
P1 P2 C3
C1 C5
C2
P3 C1 C4
Affiliation Network
O1 O2
P1 P3 Apply Clustering (Newman’s Cluster Algorithm)
P2 to find Communities
Scientific Contributions 1 2 3
People
Organizations

25. Main Network
S. Contribution
Person
Organization
Authorship
Affiliation
Citation

26. Issues
• Access to (distributed) datasets
• Provenance of data
• Experiment model (executable specification) in
order to replicate results
• Adapt the experiment model (for instance to
new metrics)

27. Thank you