1. Summa/Search and Facets
Digital Libraries à la Carte 2009
Tilburg University, 2009-07-31
Mads Villadsen <mv@statsbiblioteket.dk>

2. A Short History of
Summa/Search

3. Why?
 Improve response times
 Greater flexibility to create a system that had end user
concerns as a priority
 Expose all our resources through a single point of entry
 Combine IT knowledge with knowledge of library
science

4. How
Integrated search
 Speed and meaningful ranking of material from
different sources
Exploratory search
 Help users find what they are looking for – even if
they don't quite know it themselves
Motivational design
 Show users the information they need when they
need it

5. “Facets” – Limit Your
Search
Limit your search to certain material types

6. Facets – No Counts
Proper facets. The facet counts came later.

7. Facets Today
Proper facet counts. Only the most useful facets are shown.

8. Facet Information
Current system at the State and University Library
 9.1 million records
 8.7 million unique tags in 15 facets
 Average number of tags/document is 10
 Large facets
 Subject: 3.5 million unique tags
 Author: 4.8 million unique tags

9. How Did It All Work Out?

10. Recent Usability Test:
The Good
 The Google-like initiatives
 One simple search field
 Search suggestions
 Did-You-Mean
 The integration of extra information in the full records –
especially covers and abstracts
 The Todo list

11. Recent Usability Test:
What To Improve
 Users had trouble finding journal articles
 Many users failed to discover the facets
 Further tweaking of the relevancy ranking
 Simplification of the request procedure

12. What Now?

13. Simple Changes
 Rename “order list” to “basket”
 Make it possible to add any item to the Todo list
 Move facets to the left side
 ...

14. Search Tomorrow

15. More Data
User expectations: If they can't find it in Search, then we
don't have it.
Particularly a problem with article level data.
Will make the experience as a whole better for the end
user.

16. Rethink Facet Presentation

17. Clustering
Add clustering framework to facilitate automatic tag
extraction.
May link together articles, books, and data-sets that the
user hadn't thought of.
Should prove very useful with full text data.

18. Other
 Rights integration
 Duplicate reduction
 User specific ranking
 Further develop system and statistics tools
 Index look-ups
 And more...

19. Resources
 The hybrid library: from the users perspective
http://www.statsbiblioteket.dk/om-statsbiblioteket/brugbarhed/feltstudier
 Summa Site
http://statsbiblioteket.dk/summa/
 Search Usability Test
http://www.statsbiblioteket.dk/summa/search%20-%20usability-test-English.pdf
 Code4lib 2009 presentation on Complete Faceting
http://www.code4lib.org/conference/2009/eskildsen

20. Questions?