• Like
  • Save

Loading…

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

DISCUS: Distributed Innovation and Scalable Collaboration in Uncertain Settings

  • 1,775 views
Published

DISCUS: Distributed Innovation and Scalable Collaboration in Uncertain Settings

DISCUS: Distributed Innovation and Scalable Collaboration in Uncertain Settings

Published in Technology , Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
1,775
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
0
Comments
0
Likes
2

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Illinois Genetic Algorithms Laboratory Department of General Engineering Univ ersity of Illinois at Urbana-Champaign Urbana, IL 61801. DISCUS: Distributed Innovation and Scalable Collaboration in Uncertain Settings David E. Goldberg, Michael Welge, & Xavier Llorà NCSA/ALG + IlliGAL University of Illinois at Urbana-Champaign {deg,m-welge,xllora}@uiuc.edu
  • 2. DISCUS Outline Innovation technology for creative collaboration,  and decision-making. Combine HHC (human-human collaboration) and  HMC (human-machine collaboration) to form powerful system. Envision both synchronous and asynchronous  collaboration for continuous innovation. Overcome superficiality of online interaction  through augmented reflection
  • 3. DISCUS Components Research  Innovation support technologies (IST) – Human-based genetic algorithms (HBGA) – Interactive genetic algorithms (IGA) – Chance discovery (KeyGraphs & IDM) – Development & Integration  Collaboration technologies – Heterogeneous data source integration – Model integration and analysis – Knowledge management – Knowledge extractions and data mining –
  • 4. DISCUS Overall Picture Global Stakeholders Population (GSP) IEC Collaborative Validation Community (CVC) Ke GA yG HB ra p hs Core Coherent Innovation Team (CCIT) T2 K D2 K
  • 5. DISCUS Core Innovation Team Ke Core Coherent yG A ra BG ph Innovation Team (CCIT) H s Automated Human Agent 4 Agent 1 m1 Team Objective Knowledge Tea Broker (OKB) Human Automated Agent 2 2 Agent 3 Knowledge Exchange Architecture (KEA) Automated Human Agent 2 Agent 3 Automated T2 Agent 1 K D2 K
  • 6. DISCUS Process in a Nutshell Collect Relate • Knowledge Extraction • Knowledge Sharing (Data, Text, & Image Mining) • C ollaborative Refinement • Knowledge Management • Electronic Brainstorming • C hance Discovery • Goal Definition • Distributed C ooperation DISCUS Donate Create • C onsensus Broadcast • Innovation-based Decision Support (IDS) • Summary of the Evolved Solutions • Innovation-based Solution C reation (ISC ) • Feedback C ollection
  • 7. Extensible DISCUS Architecture DISCUS Applications DISCUS Integrated W eb Interface (DIW I) DISCUS Service Abstraction Layer Functional Scenario Definition DISCUS W ork flow Scenario Engine DISCUS Abstraction Layer Collaboration Integration Layer iGA Chance Discovery HBGA Models Visualization O n-line archiving Topic track ing/identification Core I2K T2K Them e W eaver Conference Collaboration Knowledge D2K Engine W ebcrawlers Technology Infrastructure Sharing
  • 8. DISCUS 2.0 Functionalities Group management (IlliGAL)  User information and profiling – Dynamic group formation and management – Leader identification (owner) – Discussions, models, and data sources (D2K+IlliGAL)  Flexible discussion definition – Grant/revoke access to models and data sources – Dynamic management by the discussion owner – Analysis tools (Theme Weaver + D2K + IlliGAL)  Automatic web crawlers – Document analysis –
  • 9. DISCUS 2.0 Functionalities Collaborative support  Asynchronous collaboration (bulletin boards) (KLSG) – Documents and results sharing (via attachments) (KLSG) – Synchronous collaboration (instant messaging & chat – rooms) (KLSG) On-line analysis of the discussions (IlliGAL) – Collaborative solution creation (IlliGAL) – Automated topic guided search (IlliGAL) – Connection to outside search engines – Connection to DISCUS archives – Multilanguage support for western languages (IlliGAL) – Questions and answers + bug tracker (IlliGAL) –
  • 10. Cast of 1000 Characters Powered by TRECC: This project is supported by TRECC, a program of the UIUC administered by the NCSA by the Office of Naval Research under Grant #: N00014-01-1-0175 IlliGAL: David E. Goldberg, Xavier Llorà, Naohiro Matsumura, Chen-Ju Chao, Feipeng Li, Kei Ohnishi, Tian Li Yu, Martin Butz, Antonio Gonzales ALG: Michael Welge, Loretta Auvil, Andew Shirk, Tom Redman, Duane Searsmith, Bei Yu Knowledge and Learning System Group: Tim Wentling, Andrew Wadsworth, Luigi Marini, Raj Barnerjee Minsker Research Group: Barbara Minsker, Aniruddha Bhaqwat, Wesley Dawsey, Abhishek Singh, Meghna Babbar Takagi Labo: Hideyuki Takagi University of Tsukuba GSM: Yukio Ohsawa Hakuhodo Inc.: Hiroshi Tamura, Yuichi Washida, Massataka Yoshikawa Others: Ali Yassine (GE), Miao Zhuang (GE)
  • 11. More Information Contact {xllora,deg}@illigal.ge.uiuc.edu  Visit IlliGAL web site.  http://www-illigal.ge.uiuc.edu/  http://www-discus.ge.uiuc.edu/  Recent book: Goldberg, D. E. (2002). The Design of  Innovation. Boston, MA: Kluwer Academic, http://www- doi.ge.uiuc.edu/