Presented at the Shanghai International Library Forum, Aug 24-27, 2010 by John Wang and Kevin Rundblad. Simul8 Group, UCLA Library This presentation addresses a few key areas of value creation for libraries: (1) a new workflow model for cataloging and special collections; (2) a social framework for opening crowdsourced (social) knowledge in the library online catalog; and (3) the open web architecture using APIs (internal/external) to realize new workflow and user interactions. Simul8 Group Credits: Fei Liu iCon Application Developer, UCLA PhD student Simul8 Group, UCLA Library Rajiv Tirumalareddy iCon Web Developer, UCLA Aerospace Engineering BS student Simul8 Group, UCLA Library. Alyssa Anduiza iCon Interface Designer, UCLA Design & Media BA student Simul8 Group, UCLA Library.

1. Open Web and Social Frameworks:
The Libraries’ New Frontier to
Accomplish Their Social and Cultural
Mission
Zheng (John) Wang Kevin Rundblad
Head, Web Services UX and Social Tech Strategist
UCLA Library UCLA Library

2. Library Social and Culture Functions

3. "Separately a book stock and a catalog would
be comparatively weak, but together they
possess a unique cultural potency."
‐ Pierce Bulter, Cultural Function of the Library, 1952

4. True for paper and print
paradigm

5. False for the Internet Age

6. Library Functions are Challenged

7. HTML brought content and their metadata
together onto one web page

8. Digital medium dictates how users consume content
Therefore HTML possesses a stronger cultural
potency than OPAC and its book stocks.

9. User Expectation
Immediate, efficient, and convenient access to
information

10. Library Practices
Metadata is an means to an end
Libraries offers metadata search (OPAC) as a
destination on the web

11. “… publications that are not instantly
available in full‐text will become kind of
second‐rate in a sense… become much less
visible to the reader community.”
‐ Clifford Lynch, 1997

12. Library Content Workflow Model

13. Manual metadata creation to
describe its collections

14. Resources intensive Time Consuming Back Logs
Metadata creation speed is slower than
content publishing rate

15. Call for a New Library Model
for processing and sharing collections in the Digital Age

16. Case Study: iCon (iContribute)
Fast content workflow process, web publishing, and social curation
platform

17. iCon Key Functionalities
1. Automate collection process as much as possible
Digital Camera, OCR, indexing engine, and semantic web technologies
replacing manual cataloging and metadata creation process

18. iCon Key Functionalities
1. Automate collection process as much as possible
Digital Camera, OCR, indexing engine, and semantic web technologies
replacing manual cataloging and metadata creation process
2. Social Framework
Collaborate between libraries and among users to fine tune metadata
and create new content to aid better discovery experience

19. iCon as AI
Automating Collection Processing

20. OCR
process on
Direct server
Upload Text/Images
from stored in
camera database
Digital
Imaging
iCon Searchable
full text of
pages
Using digital camera With images
And abstracted text

21. iCon Processing Rate
Process Rate
(Records/Hour/Person)
Original Cataloging 2
Copy Cataloging 6
iCon 12
Conservative Estimate

22. iCon Processing Rate
Process Rate Monthly Total
(Records/Hour/Person) (160 Working Hours/Person)
Original Cataloging 2 320
Copy Cataloging 6 960
iCon 12 1920
Conservative Estimate

23. iCon Processing Rate
Process Rate Monthly Total Yearly Total
(Records/Hour/Person) (160 Working Hours/Person) (1920 Working Hours/Person)
Original Cataloging 2 320 3840
Copy Cataloging 6 960 11520
iCon 12 1920 23040
Conservative Estimate

24. iCon Economics
Process Rate Labor Cost
Per Record Cost
(Records/Hour/Person) (Dollar/hour/Person)
Original Cataloging 2 $12.50
$25.00
Copy Cataloging 6 $4.16
iCon 12 $10.00 $0.83
Conservative Estimate

25. iCon as Social Framework
Human Computation / Crowdsourcing

26. Human Computation
Human processing for solving computational
Tasks that are considered difficult for AI, but
nominal for humans.

27. Crowdsourcing
Leveraging a community/group to collectively
produce a desired task.

28. Examples of Crowdsourcing
Wikipedia (social knowledge)
Youtube (entertainment)
Yelp (social sentiment/ratings)
StackOverflow (community knowledge)
Australian Newspaper Digitisation Program (Human Computation)

29. Is the Crowd Credible?

30. Australian Newspaper
Digitisation Program
National Library of Australia

31. 12.5 million lines of text corrected
by the public in 17 months

32. Crowdsourcing…
...handles difficult AI tasks, but nominal for human
…provides local knowledge input
…is perspectival in nature

33. Crowdsourcing = Unique Knowledge

34. Expertise is spread across
many individuals…

35. …Rather than focused
in one expert.

36. Crowdsourcing creates
favorable probabilities
for knowledge creation

37. Social Knowledge
is Local Knowledge

38. Social Knowledge
is Perspectival in Nature

39. How can Libraries leverage
the crowd?

40. Create Open Library Catalogs
to enable social knowledge

41. Social Framework Example:
The Wiki‐fication of Online
Catalog Records

42. User Generated Content (UGC)

43. User Generated Content (UGC)
Book Summaries/Description

44. User Generated Content (UGC)
User Ratings

45. User Generated Content (UGC)
User Reviews

46. User Generated Content (UGC)
Quotes from Item

47. User Generated Content (UGC)
User Tags

48. User Generated Content (UGC)
Related Links: Author/Subject based
articles, slides, video, etc…

49. User Generated Content (UGC)
The record becomes the container for
aggregating social content…

50. User Generated Content (UGC)
…and user decision point for
the value of content.

51. What are the implications
of the Wiki Book Record?

52. User Decision making is enhanced
at the record level

53. User participation
builds efficient metadata
and partial content

54. Real‐time information
sources are created from
User Generated Content

55. Real‐time information
sources are created from
User Generated Content
Searching UGC leads to discovering
Content via new channels

56. People like to do/see what
others are doing…
Concept: “Social Proof”

57. All UGC From book records
is output in real‐time feed

58. Think of it as a Twitter
for your Online Catalog

59. iCon Architecture

60. iCon Architecture
1. Open Data
Programmable by 3 party users

61. iCon Architecture
1. Open Data
Programmable
2. Open Process
Crowdsourcing for processing collections and enrich collections

62. iCon Architecture
1. Open Data
Programmable
2. Open Process
Crowdsourcing , User Curation
3. Open Platform
PaaS and SaaS: allow users to freely add content and provide
users with mashup services

63. iCon Architecture
1. Open Data
Programmable
2. Open Process
Crowdsourcing , User Curation
3. Open Platform
PaaS and SaaS: allow users to freely add content and provide users with mashup
services
4. Open Standards
HTML based for scalability

64. iCon Architecture
1. Open Data
Programmable
2. Open Process
Crowdsourcing , User Curation
3. Open Platform
PaaS and SaaS: allow users to freely add content and provide users with mashup
services
4. Open Standards
HTML based for scalability
5. Open Source Where Possible
use open source software to keep the cost down

65. Open Web = Library Values
• Libraries promote free information sharing
• Libraries are places for remixing and
recreating content
Open Web rejuvenates libraries to continue
social and culture functions in the Internet
Age.

66. Questions
Zheng (John) Wang Kevin Rundblad
Head, Web Services UX and Social Tech Strategist
Co‐Manager Simul8 Group Co‐Manager Simul8 Group
UCLA Library UCLA Library
zhengwang@library.ucla.edu kevinrundblad@library.ucla.edu