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.

1. SMIRP
Jean-Claude Bradley
bradlejc@drexel.edu
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
http://smirp.drexel.edu

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. Human
Agent
Autonomous
Agent
SMIRP
Automation
SWAT team
(Bot)
Browser
Excel
The SMIRP model for a hybrid Human/Autonomous Agent System
Anthropomimetic
Design

4. Approaches to Collaborative Electronic Notebooks
rigid
SMIRP
compromise:
Rigid information representation
Flexible linking of modules
flexible
•Structured
•Generally
domain
specific
•Adaptable
•Unstructured
http://smirp.drexel.edu

5. Add information
to database
Retrieve
information
Modify
database
structure
Functional Requirements of a collaborative electronic notebook
SMIRP
Request
structural
modification
http://smirp.drexel.edu

6. Two approaches to the development of databases
Communicate
anticipated
need
Design
database
structure
Let database structure
evolve
through useSMIRP
http://smirp.drexel.edu

7. Fundamental Information Representation in SMIRP
Module 1 Module 2
Parameter 1
Parameter 2
Parameter 4
Parameter 5
instance
Record 1
instance
Record 2
http://smirp.drexel.edu
(People)
(Name)
(Employee of)
(Company)
(Name)
Parameter 3(email)
(Address)
Bill Gates Microsoft

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
http://smirp.drexel.edu

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
http://smirp.drexel.edu

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
http://smirp.drexel.edu

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%
http://smirp.drexel.edu

12. Recruitment
events 2%
Project
Manager
5%Errors
5%
Productivity
Tracking
14%
People 28%
Workstudy
hours reporting
46%
Most Active Human Resource Management Modules
http://smirp.drexel.edu

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%
http://smirp.drexel.edu

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
http://smirp.drexel.edu

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. 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. Active
E-mail
Browser
Excel
Interface
Information Filter
TimeKeyword User ContextSimilarity
Passive
Operation
SMIRP Information Retrieval Matrix

18. Keyword Search Results: example “nanotube”
Active Information Retrieval : keywords

19. From Keyword to Article

20. From Keyword to Knowledge Filter

21. From Keyword to Orders

22. From Keyword
to Protocol
Prototyping

23. Active
Information
Retrieval :
Time
Filtered

24. Active Information Retrieval: User and Operation Filtered Search
Autonomous Agent
Monitoring

25. Active Information Retrieval: Similarity Based

26. Active Information Retrieval: Context Based

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. Module-Level Alerts: Creation of an alert for new urls to articles

29. Module Level Alerts: Creation of an alert for new urls to articles

30. Space Level Alerts: example of keyword filtering

31. Seamless Integration of Human and
Autonomous Agents in Workflows
Real-Time Workflow Designs
Automated
Human
(default)
State A State B

32. Workflow for Extraction of Article information and
url
Queries Web and
extracts information

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. 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. Pre-emptive information retrieval
Finding documents that should be of interest to current work

36. Pre-emptive information retrieval

37. Pre-emptive information retrieval
Finding documents that should be of interest to current work

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. 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. Acknowledgements
Benjamin Samuel
Sundar Babu
Raj Hooli
Ketan Patel
Mohammad Haghkar
NSF CAREER CHE-9875855
CIA
http://smirp.drexel.edu