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PERICLES Technical Appraisal Tool - ‘Eye of the Storm: Preserving Digital Content in an Ever-Changing World’

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This presentation was delivered by Simon Waddington from PERICLES project partner King’s College London at the interactive workshop ‘Eye of the Storm: Preserving Digital Content in an Ever-Changing World’ (Wellcome Collection Conference Centre, London, 2 December 2016).
This full-day event aimed at introducing and experimenting with the PERICLES model-driven approach demonstrating its usefulness for managing change in evolving digital ecosystems.

http://pericles-project.eu/

Published in: Technology
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PERICLES Technical Appraisal Tool - ‘Eye of the Storm: Preserving Digital Content in an Ever-Changing World’

  1. 1. GRANT AGREEMENT: 601138 | SCHEME FP7 ICT 2011.4.3 Promoting and Enhancing Reuse of Information throughout the Content Lifecycle taking account of Evolving Semantics [Digital Preservation] “This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no601138”. Simon Waddington (King’s College London) Jun Zhang (King’s College London)
  2. 2. Overview  Introduction  Appraisal of digital objects at Tate  Functionality  Features  Architecture and implementation
  3. 3. Introduction  Technical appraisal is the process of determining the (on-going) feasibility of preserving the digital objects ◦ Maintenance in a reusable form ◦ Takes into account obsolescence of software, formats etc.  Aim to produce a general purpose tool ◦ Conference demonstrator is for digital video
  4. 4. Digital video art at Tate  Circa 500 video artworks, increasing at c. 50 works per year  Limited (though increasing) and familiar technologies. In-house experience and regular use.  Production environment similar to quality checking environment.  Existing network of experts  Common technologies within the communities of Practice Bruce Nauman, Violent Incident 1986 (T06732)
  5. 5. Features Demonstrates use of:  External data sources to estimate risk (e.g. obsolescence, hardware failure) ◦ Search engines ◦ Software repositories ◦ Wikipedia  Ecosystem models ◦ PERICLES Digital Video Art (DVA) (extended) ontology and Linked Resource Model (LRM) ◦ Captures dependencies between artwork components ◦ Represents expert knowledge about video playback
  6. 6. Functionality  Collection and object level risk assessment for complex digital objects ◦ Support proximity estimation and confidence estimates  Component level risk analysis ◦ Identify high-risk components across collections ◦ Provide graphical views  Object-level risk analysis ◦ Presents risks to individual components in an object ◦ Determine potential recoverability actions  Link to MICE tool for impact visualisation (in progress)
  7. 7. Architecture and implementation Service Layer External Data Sources Storage Layer User Interface Web Server Metadata Extraction Statistical Analysis Risk- impact Analysis Instance Store Knowledge Base Data Harvester  Java web services framework based on Apache Tomcat  Components written in R and Python  User interface uses HTML5, JavaScript and CSS3  Due for release – first quarter of 2017

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