1. Automation in Digital Preservation: Three Scenarios Milena Dobreva 1, Yunhyong Kim 2, Gillian Oliver 3, Seamus Ross 2, Raivo Ruusalepp 4 1 Centre for Digital Library Research 2 Digital Curation Center (DCC) & Humanities Advanced Technology and Information (HATII), University of Glasgow, UK 3 School of Information Management, Victoria University of Wellington, Wellington, New Zealand 4 Estonian Business Archives Consultancy, Algi 29, Tallinn 10620, Estonia

2. UK e-Science ALL HANDS meeting: 10 September 2008 2 Talk overview 3 2 1 Automation in digital preservation Three case studies Findings What comes next?

3. UK e-Science ALL HANDS meeting: 10 September 2008 3 “… if we do not actively pursue the preservation of digital material now, we risk having a gap in our intellectual record. … If you allow me another historical reference, we do not want to experience the digital equivalent of the destruction of the Alexandria Library. Scientific assets are just too valuable to be put at risk”. Ms. Viviane Reding, EU Commissioner on IS & Media “The digital preservation community’s inability to bring firm evidence to bear in support of its contentions about data loss, coupled with the alarmist rhetoric of terms such as digital dark ages and digital black hole, leave us exposed.” Prof. Ross Harvey, Charles Sturt University, NSW, Australia The Preservation Landscape

4. UK e-Science ALL HANDS meeting: 10 September 2008 4 1 Automation in digital preservation Digital preservation –part of the information management –highly interdisciplinary What actually ‘interoperability into the future’ means? –preservation of the bit streams –preservation of semantics A coherent theory of preservation is still not developed –need justified in 2001 –currently CASPAR and SHAMAN projects are working in this direction

5. UK e-Science ALL HANDS meeting: 10 September 2008 5 Typical preservation issues… Failure of any component of the technological chain Hardware, software or support environment change. Outcomes of a project which is not sustained. Problems with the “the bits” This could happen because of a storage device or medium failure; or if a DNS entry is no longer resolvable. Changes in the Knowledge Base Loss of understanding or usability Lost data on provenance or authenticity (requirements of trusted repositories) Record of who did what and how did they do it Strategies: migration, emulation, digital archaeology, …

6. UK e-Science ALL HANDS meeting: 10 September 2008 6 DigitalPreservationEurope: Research Roadmap (2007): 9 themes 1.Restoration 2.Conservation 3.Collection and Repository Management 4.Preservation as Risk Management 5.Preserving the Interpretability and Functionality of Digital Objects 6.Collection Cohesion and Interoperability 7.Automation in Preservation 8.Preserving the Context 9.Storage Technologies and Methods

7. UK e-Science ALL HANDS meeting: 10 September 2008 7 Functional Model: Open Archival Information System (OAIS)

8. UK e-Science ALL HANDS meeting: 10 September 2008 8 2 Three case studies Build upon project experiences of HATII at the University of Glasgow and partners –Appraisal –Metadata extraction based on genre classification –Risk assessment and audit

9. UK e-Science ALL HANDS meeting: 10 September 2008 9 Appraisal Approach: developed sets of appraisal criteria; work on automation of their evaluation continues.

10. UK e-Science ALL HANDS meeting: 10 September 2008 10 Metadata Extraction Based on Genre Classification

11. UK e-Science ALL HANDS meeting: 10 September 2008 11 Risk Assessment and Audit DRAMBORA (Digital Repository Audit Method Based on Risk Assessment)

12. UK e-Science ALL HANDS meeting: 10 September 2008 12 The Three Case Studies as OAIS Functional Entities

13. UK e-Science ALL HANDS meeting: 10 September 2008 13 SWOT observations

14. UK e-Science ALL HANDS meeting: 10 September 2008 14 3 Findings These case studies present various functions in the sense of OAIS model. For such ‘smaller’ digital preservation solutions we need to know more on the common logic. For bigger applications a coherent theory will be helpful. The degree of automation differs! –Profiler –Single automated model –Hybrid model

15. UK e-Science ALL HANDS meeting: 10 September 2008 15 Questions? milena.dobreva@strath.ac.uk