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Knowledge Architecture: Graphing Your Knowledge

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How NASA uses graph database technology to power knowledge transfer and lessons learned.

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Knowledge Architecture: Graphing Your Knowledge

  1. 1. © 2015 IHS. ALL RIGHTS RESERVED. KNOWLEDGE ARCHITECTURE: GRAPHING YOUR KNOWLEDGE Combining Strategy, Data Science and Informatics to Transform Data to Actionable Knowledge Government Graph Day February 28, 2017 David Meza Chief Knowledge Architect NASA Johnson Space Center
  2. 2. AGENDA • Challenges • Knowledge Architecture • Graphing LLDB • Questions? 2
  3. 3. “The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker.” PETER F. DRUCKER, 1999
  4. 4. 30%of total R&D spend is wasted duplicating research and work previously done. Source: National Board of Patents and Registration (PRH), WIPO, IFA 54%of decisions are made with incomplete, inconsistent and inadequate information Source: InfoCentric Research 46% Workers can’t find the information they need almost half the time. Source: IDC Challenges
  5. 5. To convert data to knowledge a convergence of Knowledge Management, Informatics and Data Science is necessary. 5 Knowledge Management Data ScienceInformatics
  6. 6. Knowledge Architecture • The people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations. • What is an intellectual infrastructure? • The set of activities to create, capture, organize, analyze, visualize, present, and utilize the information part of the information age.. • Information + Contexts = Knowledge • Knowledge Management + Informatics + Data Science = Knowledge Architecture • KM without Informatics is empty (Strategy Only) • Informatics without KM is blind (IT based KM) • Data Science transforms your data to knowledge 6
  7. 7. “We have an opportunity for everyone in the world to have access to all the world’s information. This has never before been possible. Why is ubiquitous information so profound? It is a tremendous equalizer. Information is power.” ERIC SCHMIDT (FORMER CEO OF GOOGLE)
  8. 8. There was a inquisitive engineer…
  9. 9. LESSON LEARNED DATABASE 10 2031 lessons submitted across NASA. Filter by date and Center only. Useful information stored in database.
  10. 10. Document to Graph 11
  11. 11. PATTERNS EMERGE
  12. 12. TOPIC MODELING 13 Topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. LDA Model from Blei (2011) David Blei homepage - http://www.cs.columbia.edu/~blei/topicmodeling.htmlBlei, David M. 2011. “Introduction to Probabilistic Topic Models.” Communications of the ACM.
  13. 13. CORRELATION BY CATEGORY 14 To find the per-document probabilities we extract theta from the fitted model’s topic posteriors
  14. 14. TOPIC TRENDS 15 Using mean of theta by years to trend topics
  15. 15. TOPIC VISUALIZATION 16
  16. 16. GRAPH MODEL OF LESSON LEARNED DATABASE 17 http://davidmeza1.github.io/2015/07/16/Graphing-a-lesson-learned-database.html
  17. 17. GRAPH MODEL OF LESSON LEARNED DATABASE 18
  18. 18. GRAPH MODEL OF LESSON LEARNED DATABASE 19
  19. 19. DATA DRIVEN VISUALIZATION 20
  20. 20. 26 WHAT COULD YOU ACCOMPLISH IF YOU COULD: • Empower faster and more informed decision-making • Leverage lessons of the past to minimize waste, rework, re-invention and redundancy • Reduce the learning curve for new employees • Enhance and extend existing content and document management systems
  21. 21. Contact Information David Meza – david.meza-1@nasa.gov Twitter - @davidmeza1 Linkedin - https://www.linkedin.com/pub/david-meza/16/543/50b Github – davidmeza1 Blog davidmeza1.github.io 27
  22. 22. Contents © 2015 IHS. ALL RIGHTS RESERVED. 28 Report Name / Month 2015 QUESTIONS?

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