How Databases LearnAndrea K. Thomer

Michael B. Twidale

Graduate School of Library and Information Science

University of Illinois at Urbana-Champaign

5 Mar 2014 – iConference - Berlin

Ceci n’est pas un database.

From ethnography to trace ethnography

Hine 2006, Bietz & Lee 2009 – traditional ethnographies of scientific databases

Schuurman, 2007: “database ethnographies”

Geiger & Ribes, 2011: document-driven ethnography -> trace ethnographies looks at "how, where, and by whom [documents] are

produced, edited, revised or filed" -- in a database this is much the same but we ask the same questions of tables and fields

Study of edits on Wikipedia 1000’s of agents making 1000’s of changes over hours

Our study: 1000s of changes by few people over many years; looking for traces of schema change, reappropriation of fields

We ask: how do databases, like buildings, learn?

Case study: the Universal Chalcidoidea Database

Parasitic wasps: “gem-like inhabitants of the woodlands heretofore unknown and by most never seen nor dreamt of.” – Girard, 1924

How we learned the database

How the database learned:

“CRENCYRT”

How the database learned: • Additional

table added for separate project

• Duplicate, non-normal tables as an ad hoc way to manage workflow

“REFNEW”

Brand’s concept of shearing

Stuff (days – months)

Space plan (months to years)

Services (years – decades)

Skin (decades)

Structure (decades to centuries)

Site (eternal)

How do we account for shearing in databases?

Additional questions: What makes a database able to adapt to changing uses and

users?

How does a database evolve when the people in charge of it change?

How are fields repurposed over time?

How is it that some databases adapt to changing needs and circumstances better than others?

How are major renovations (refactorings) best handled?

How do we work with unchangeable traces of earlier designs?

How and when are “best practices” not actually for the best?

Conclusions (and more questions)

For databases, thinking about preservation as a simple binary between migration and emulation is too simplistic They need to evolve, but how? Buildings face a similar challenge – can we gain insights

from comparison?

Questions for you: From Mike: Do you know of more examples of databases

that have gone through gradual tweaks or punctuated leaps?

From me: What else should we be reading?

Thank you!Refs:Bietz, M., & Lee, C. (2009). Collaboration in metagenomics: Sequence databases and the organization of scientific work. ECSCW 2009, (September), 7–11. Retrieved from http://www.springerlink.com/index/t7124470143464r9.pdfBrand, S. (1995).  How Buildings Learn: What Happens After They’re Built. Penguin Books.Girault, A.A. (1925). “Some Gem Like Inhabitants of the Woodlands by Most Never Seen Nor Dreamt Of.” The Literature of Platygastroidea. Retrieved from http://plazi.org:8080/dspace/handle/10199/15794Geiger, R. S., & Ribes, D. (2011). Trace Ethnography: Following Coordination through Documentary Practices. 2011 44th Hawaii International Conference on System Sciences, 1–10. doi:10.1109/HICSS.2011.455Hine, C. (2006). Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work. Social Studies of Science, 36(2), 269–298. doi:10.1177/0306312706054047Schuurman, N. (2008). Database Ethnographies Using Social Science Methodologies to Enhance Data Analysis and Interpretation. Geography Compass, 2(5), 1529–1548. doi:10.1111/j.1749-8198.2008.00150.

Acknowledgements: thanks to Katrina Fenlon, Nic Weber and Karen Wickett for feedback; and thanks to CIRSS for funding

Databases at RLB