Automated Experimentation in Social Informatics


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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

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Automated Experimentation in Social Informatics

  1. 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. 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. 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. 4. Early work (ca. 1990)
  5. 5. What we were missing • Computational power • Appropriate level of abstraction • Appropriate specification languages
  6. 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. 7. Use case 1: Automated Experimentation in eResponse
  8. 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. 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. 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. 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. 12. Poll-reporter LCC interaction model
  13. 13. A Flood Case Study: sensor network
  14. 14. A Flood Case Study Evacuation •  Agents (e.g., emergency subordinates such fire-fighters) move to specific locations assigned by the coordinator
  15. 15. The e-Response System Architecture
  16. 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. 17. Experimental Results
  18. 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. 19. Use case 2: Scientific Community Discovery
  20. 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. 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. 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. 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. 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. 25. Main Network S. Contribution Person Organization Authorship Affiliation Citation
  26. 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. 27. Thank you