SMIRP Barnett 2002


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Jean-Claude Bradley presents on "SMIRP: Effective use of a self-evolving database for information capture and retrieval in an R&D environment" on August 14, 2002 at the Barnett International Conference on Laboratory Notebooks. Specific implementations of integrating human and automated workflows in chemistry and nanotechnology applications are detailed.

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SMIRP Barnett 2002

  1. 1. SMIRP Jean-Claude Bradley Drexel University Barnett International Conference on Laboratory Notebooks 08/14/2002 Effective use of a self-evolving database for information capture and retrieval in an R&D environment
  2. 2. LIMS CENS Single Instrument Automation Laboratory Information Management Systems Collaborative Electronic Notebook Systems Human /Autonomous Agent Hybrid Systems Human Managed Fully Autonomous Scientific Research Systems TODAY SMIRP bridge The Evolution of Automation in Scientific Research
  3. 3. Human Agent Autonomous Agent SMIRP Automation SWAT team (Bot) Browser Excel The SMIRP model for a hybrid Human/Autonomous Agent System Anthropomimetic Design
  4. 4. Approaches to Collaborative Electronic Notebooks rigid SMIRP compromise: Rigid information representation Flexible linking of modules flexible •Structured •Generally domain specific •Adaptable •Unstructured
  5. 5. Add information to database Retrieve information Modify database structure Functional Requirements of a collaborative electronic notebook SMIRP Request structural modification
  6. 6. Two approaches to the development of databases Communicate anticipated need Design database structure Let database structure evolve through useSMIRP
  7. 7. Fundamental Information Representation in SMIRP Module 1 Module 2 Parameter 1 Parameter 2 Parameter 4 Parameter 5 instance Record 1 instance Record 2 (People) (Name) (Employee of) (Company) (Name) Parameter 3(email) (Address) Bill Gates Microsoft
  8. 8. Case-study: Evolution of SMIRP structure in a chemistry laboratory Location Drexel University Department of Chemistry Users faculty, undergraduate students, graduate students, librarians and other university personnel Period Feb 1999 – April 2001, with a detailed focus on last 7 months (Sept 2000-April 2001) Total accounts (last 7 months) 78 Active Accounts (added records) 50 Administrators (changed database structure) 9
  9. 9. Human Resource Management 13% Maintenance 1% Knowledge Processing 72% Most Active Module Categories (9/00 – 4/01) Labwork 14% 118 modules 1/3 account for 98% of activity
  10. 10. Most Active Knowledge Processing Modules Journal 9% Knowledge Filter 3% Reformat Reference requests 20% Find Reference 66% Publisher Document Production Reference Processing Parameter Correlation Data source files Experimental Conclusion Generation Knowledge consolidation
  11. 11. Most Active Laboratory Modules Preparation of Silver rods for SCBE TEM Micrographs Of Pd on C SCBE on membranes Hydrogenation of Crotonaldehyde using Pd Catalysts Reduction of Methylene blue by Pd Metal Particles in a Field Electrodeposition of Pd on Graphite 29% Protocol Prototyping 25% Pd onto Carbon Nanofibers 17% Electroless plating on Membranes 9% Synthesis of Pd catalysts by Bipolar electrochemistry 5% TEM Micrographs Of Pd on C 3% Pd particle size analysis using TEM 3%
  12. 12. Recruitment events 2% Project Manager 5%Errors 5% Productivity Tracking 14% People 28% Workstudy hours reporting 46% Most Active Human Resource Management Modules
  13. 13. Most Active Maintenance Modules SMIRP Problems 22% Orders 19% Invoice (TEM/SEM and other instrument charges) 19% Laboratory materials 16% Vendor 15% Order forms 9%
  14. 14. Activity Analysis by Category over Time 2000-10-3 2000-10-17 2000-10-30 2000-11-12 2000-11-25 2000-12-8 2000-12-21 2001-1-3 2001-1-16 2001-1-30 2001-2-12 2001-2-25 2001-3-10 2001-3-23 2001-4-5 2001-4-18 Maintenance Human Resource Management Laboratory Work Knowledge Processing 0 1000 2000 3000 4000 5000 6000 7000 8000
  15. 15. For agents to make a decision to: ACT NOT ACT Generally for quality controlExpected information: Retrieve details and execute from a menu of predefined tasks Unexpected information: Redesign tasks This could be absence of information: “No News is Good News” WHY retrieve information?
  16. 16. Active Passive Negative (implied) Pre-emptive I want to know something NOW Keep me updated regularly with new information No news is good news Tell me things I SHOULD want to know but have not asked for Burden on Agent Highest Lowest Are your closest family members alive? A competitor has initiated research in my market space New experiments in a particular project Obtaining a phone number Description ExampleMode HOW Agents Retrieve Information
  17. 17. Active E-mail Browser Excel Interface Information Filter TimeKeyword User ContextSimilarity Passive Operation SMIRP Information Retrieval Matrix
  18. 18. Keyword Search Results: example “nanotube” Active Information Retrieval : keywords
  19. 19. From Keyword to Article
  20. 20. From Keyword to Knowledge Filter
  21. 21. From Keyword to Orders
  22. 22. From Keyword to Protocol Prototyping
  23. 23. Active Information Retrieval : Time Filtered
  24. 24. Active Information Retrieval: User and Operation Filtered Search Autonomous Agent Monitoring
  25. 25. Active Information Retrieval: Similarity Based
  26. 26. Active Information Retrieval: Context Based
  27. 27. Passive Information Retrieval: Email Alerts Space Level Module Level All Activity New Entries When link to article has been found Monitor progress of software development Keep track of which software version users have downloaded Monitor which experiments are being investigated Keep track of special users: Job applicants Former users Collaborators Updates on report or article being written (general) (specific) New activity related to keywords Quality control of autonomous agent activity Quality Control of workflows
  28. 28. Module-Level Alerts: Creation of an alert for new urls to articles
  29. 29. Module Level Alerts: Creation of an alert for new urls to articles
  30. 30. Space Level Alerts: example of keyword filtering
  31. 31. Seamless Integration of Human and Autonomous Agents in Workflows Real-Time Workflow Designs Automated Human (default) State A State B
  32. 32. Workflow for Extraction of Article information and url Queries Web and extracts information
  33. 33. Autonomous Agent Successful Processing Citation to be Processed Portal Not Found Citation Invalid Information Missing Human Agent Handles exceptions Human/Autonomous Agent Coordination in Workflow
  34. 34. Pre-emptive information retrieval Report experimental results Read experimental results Generate Search Strings Read search strings Report on search results Alerted to new documents of potential interest Parsebot Googlebot Finding documents that should be of interest to current work
  35. 35. Pre-emptive information retrieval Finding documents that should be of interest to current work
  36. 36. Pre-emptive information retrieval
  37. 37. Pre-emptive information retrieval Finding documents that should be of interest to current work
  38. 38. Leveraging and Extending Bot Implementation Citation bot in other laboratory research and teaching spaces In online class SMIRPspace: Plagiobot System Automatic Content Summarization Tools Analysis/verification of experimental data analysis Conversion from Passive to Negative Information Mode: Bot Monitoring of other Bots Monitoring of competitor/collaborator activity (patents/papers) Automatic Keyword Generation from most frequently used or read words
  39. 39. Conclusions This is still a “Human World” SMIRP can serve as a framework to allow Human and Autonomous Agents to operate freely within a Laboratory Research Collaborative Space Automation within workflows can be accelerated by creating Autonomous Agents that are more Human- like in how they retrieve and store information
  40. 40. Acknowledgements Benjamin Samuel Sundar Babu Raj Hooli Ketan Patel Mohammad Haghkar NSF CAREER CHE-9875855 CIA
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