This presentation was delivered by Simon Waddington from PERICLES project partner King's College London at the 12th International Digital Curation Conference (IDCC17).

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]
Simon Waddington (King’s College London)
Technical appraisal and change
impact analysis

2.  Appraisal
◦ Aims to determine which data should be kept by an
organisation
◦ Traditionally performed prior to transfer to an archive
◦ Guided by policies based on defined criteria
 Technical appraisal
◦ Evaluation of the (on-going) feasibility of preserving the
digital objects
◦ Answers the question “can we preserve?”

3.  Simple digital objects
◦ E.g. files, software applications, operating systems
◦ Include hardware specification
 Complex digital objects
◦ Digital objects made by combining a number of simple
digital objects
 Dependency
◦ Relationships between components of a complex digital
object
◦ Functional relationship

4.  Digital video artwork  Science experiment object
Video codec Container
Media player
Operating system
Computer
Digital video
Document ViewerImage Viewer
Image File
Scripting Language
Database
Document File

5.  Complex digital objects subject to changing
external environment
◦ Technical appraisal required on an ongoing basis to
support long term reuse
 Reusability implies complex digital objects
may need to be adapted
◦ Potential adaptations termed recovery options
◦ Significant properties – specify what features should be
maintained
 Main risk considered is availability
◦ Obsolescence
◦ Hardware failure

6.  Is this the Flying Scotsman?
◦ Cost of the restoration £4.5 million from 2006–2016

7.  Digital video artwork
◦ Comprises videos and their surrounding technical environment
◦ Video codec, audio codec, subtitles, container, media player,
operating system, computer, display
 Mary - digital art conservator
◦ Supports acquisition decisions
◦ Maintains artworks for exhibition
◦ Has limited technical knowledge of video
◦ Has no control over the technologies used by artists
 Artworks are required for ongoing display
◦ Adapt artwork to current technical environment
◦ Maintain viewing experience rather than use of specific technologies
◦ Potentially exist in multiple versions
 Artworks may be maintained indefinitely
Sow Farm by John Gerrard

8.  Space science experiment
◦ Raw data captured by instrument, stored in database
◦ Scripts written by scientists to process raw data
◦ Image files and documents generated by scripts
 Steve – space science data manager
◦ Responsible for maintaining data from multiple experiments
◦ Little or no control on the technologies used by scientists
◦ Large volumes of experiments to deal with
 Examples
◦ Earth observation, solar measurements, material science, cell biology
◦ Often time-related and expensive/impossible to replicate
 Reuse – continuing over long timeframes
◦ Compare performance of different instruments
◦ Compare processing techniques
◦ Determine long term trends e.g. in solar activity
◦ Deal with errors and anomalies

9.  What are the external risks to a complex
digital object?
 What are the proximity and impact of those
risks and what are the recovery options?
 Implementation of the chosen recovery
option

10.  Maintain inventory of artworks and
components
◦ Video formats, players, operating systems etc.
 Monitoring the external environment
◦ Aka preservation watch
◦ Monitors websites and external news sources
◦ Networks with fellow conservators
 Technical analysis
◦ Records technical specifications of components
◦ Learns from practical experience of testing

11.  External monitoring is time-consuming and
unreliable
◦ E.g. QuickTime formats
 Hard to plan forward
◦ Sudden unavailability of a component hard to predict
rigorously
◦ May imply a large amount of work if a technology is used in
many artworks
 Compatibility of components
◦ Based on human experience rather than a systematic model
 Difficult in determining recovery options
◦ Time-consuming analysis and testing of many options

12.  Large variety of scripting languages and formats
used by scientists
◦ No control of the technologies used
 Unable to warn scientists that their experiments
may need to be updated to maintain reusability
 Can’t support scientists who want to rerun a
particular experiment
◦ E.g. provide information on website
 Unfamiliar with older technologies

13.  Normalisation
◦ Convert objects to one or more “long-lived” formats
◦ Performed systematically on all objects at acquisition
 Problems
◦ Objects may discarded before they require any adaptation
◦ Objects may already be sufficiently “future proof”
◦ May imply major re-engineering, whereas only minor changes
are sufficient
◦ Could increase risks if wrong choices are made
 Freezing
◦ E.g. virtualisation
◦ Software licensing, security and compliance issues
◦ May be impossible to source suitable hardware
◦ May not be acceptable to users e.g. scientists

14.  Automated tool to assist in appraisal
 Main features
◦ Automated harvesting of environmental data and trend
analysis
◦ Pre-built domain models for digital video and space science
experiments
◦ Collection-level risk, proximity and impact analysis
◦ Component-level risk, proximity and impact analysis
◦ Object-level analysis and determination of recovery options
 Storage
◦ Tool creates a registry of objects
◦ Objects themselves are not stored in the tool

15.  Applied in industries such as aviation
 Determine availability of hardware
components
 Standardised
lifecycle
model for a
technology
◦ Units shipped
against time

16.  Compute lifecycle curve from harvested data
◦ Software repositories e.g. commits and downloads
◦ Search engines
◦ Wikipedia
◦ Usage tracking data
◦ Social networks
 Confidence measure
◦ Correlate results across different data sources
 Calibration
◦ Compare results with known dates e.g. operating systems
 Validation
◦ Operating systems have known end of support dates
◦ Predict start date from incomplete time series

17. 2012 2014 2016 2018 2020 2022 2024
Video
codec
Container
Media
player
Operating
system
Computer
Current
obsolescence
Recovery
option 1
Recovery
option 2
Recovery
option 3

18.  Representation of the entities and dependencies
◦ OWL ontology
◦ Scope - decision about what to leave in and what to leave out
 Layered model
◦ Domain-independent ontology (Linked Resource Model) to
describe change
◦ Domain-dependent ontology – describes e.g. video
components
 Inherits from existing domain ontologies (e.g. CIDOC-CRM)
 Modular
◦ Supports reuse in different applications
◦ Ontology design patterns

19.  Describes the compatibility between instances
◦ E.g. media player X and video codec Y
 Does not guarantee compatibility
◦ Recoverability options require testing and validation
◦ Enables alternatives to be excluded
 Features
◦ Supports full and partial compatibility
◦ Instances added by hand – currently command line tool
◦ Needs to be updated over time
◦ Two prebuilt ontologies provided

20.  Reflects the cost of transforming entities of
the same type
◦ E.g. change media player from Mplayer to Xine
 Currently built by hand using command line
tool
 Needs to be adapted to specific context and
updated over time

21.  Use ontology to populate a probabilistic
graphical model
◦ States are components in complex digital object
 Exhaustive analysis very costly
◦ Apply a variation of Pearl’s Belief Propagation Algorithm
◦ Based on efficient message passing
 Generate recovery options
◦ Correspond to different temporal constraints

22.  Based on web
services
 Java – UI
framework
 Analysis
components in
Python and R
 Triple store
◦ Fuseki or
PERICLES ERMR

23.  The technical appraisal tool is not a
repository or archive
 Central point is the ERMR (Entity Registry
Model Repository)
 Objects (composed of files, software,
hardware descriptions)
◦ Retained across multiple storage systems
◦ Those storage systems may or may not be repositories or
archives

24.  Model Impact Change Explorer (MICE)
◦ Visualisation tool using D3 Javascript library
◦ Enables users to evaluate how a potential change to a
resource will impact the overall ecosystem
◦ Changes described via “deltas”
◦ uses PERSiST, an intermediate component for semantic
interpretation of the DVA ontology

26. MICE-Appraisal Tool Integration
Workflow
Engine
PERSIsT API
retrieves
dependencies
and impact
forwards
Change (LRM delta)
visualises
impact
accepts /
rejects
change
Entity Registry Model Repository (ERMR)
saves
change
Technical Appraisal
Tool
recovery
options
inserts
new
Media /
selects
recovery
option
returns user’s
decision
sends change
(RDF triples)
retrieves
dependencies
and costs writes
recovery
options

27.  PERICLES Appraisal Tool
◦ Due for release in March 2017
◦ Release on Github
 PERICLES MICE tool
◦ Available on Github at
https://github.com/pericles-project/MICE
 Licences
◦ Apache License Version 2.0, January 2004
◦ http://www.apache.org/licenses/

28.  Demonstrates an automated decision support for
technical appraisal
 Data-driven approach to monitor environmental
trends
 Ecosystem model to capture technical
information on dependencies
 Integrated tools for presenting risk-impact
analysis, impact visualisation and recoverability
options