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”.

APPROACH TO TECHNICAL APPRAISALSimon Waddington (King’s College London)

Technical appraisal

Case studies

Appraisal process

Data-driven environment analysis

Dependencies and change models

Technical appraisal tool

Overview

Complex digital objects◦ Made up of interdependent components ◦ Files, software, hardware, user communities, policies◦ Examples from digital media art and space science

Technical appraisal◦ Concerned with question: “Can we preserve?”◦ How much of my digital content is usable and for how long?◦ Problem for curators/conservators/owners but also users

e.g I see a complex object in a repository – what are the chances I can still use it?

Continuum approach◦ Don’t separate active life from archival◦ Potentially non-custodial

Technical appraisal

Video and software based art◦ Comprise hardware and software elements (e.g.

media files, containers, media players, proprietary and commercial software, operating systems, computers, displays)

◦ May involve cutting edge technology

Reuse in different installation settings◦ Artworks may exist in multiple versions◦ Freezing current state e.g. through virtualisation

may not be an option◦ Normalisation incurs work for potentially no benefit

Technical appraisal◦ Assessment of value

Video/Software-based art

Sow Farm by John Gerrard

Brutalism, by Jose Carlos Martinat

Science data originating from International Space Station

Preserve data, software, parameters and documentation ◦ Support interpretation and validation◦ Repeatable science◦ Design of future experiments

Long timeframes of experiments Requirement to keep observations

indefinitely Ongoing reuse and validity

checking

Science experiments

Depends on expert knowledge of human curators or conservators◦ Monitoring technology changes – e.g. trends in software usage and

availability E.g. Apple QuickTime

◦ Usage patterns Who is using the content and what are their requirements? Trends in interest in specific content types

◦ Technical knowledge E.g. Can video player X play videos in format Y? If not what are the best

alternatives? Issues

◦ Lack of technical knowledge◦ Failure to identify trends◦ Increasing volume of digital content

Human appraisal

Appraisal process

Data-driven methods enable changes to be monitored and predicted

Extract time series for analysis Sources

◦ Software repositories e.g. commits and downloads◦ Search engines◦ Wikipedia◦ Usage tracking data◦ Social networks

Correlate results across different data sources

Monitoring and modelling the environment

Digital ecosystems and change● Digital ecosystem

● Digital objects, technical services, policies, processes, user communities

● Behavioural change ● E.g. technological change, policy change, which have an impact on other entities

through dependenciesGiven objects A and B. A is dependent on B if changes to B have a significant impact on the state of A, or if changes to B can impact the ability to perform function X on A.

Depends onEntity A Entity B

● Layers of models● Linked Resource Model (LRM) – upper ontology for change● Domain ontology – e.g. DVA – Digital Video Art

Ecosystem models – DVA ontology

AbstractResource

intersectionOf (ConcreteResourcAggregatedResource)

ConcreteResource 1

ConcreteResource 2

ConcreteResource N

realizedAs

hasParthasPa

rt

hasPart

lrm:Resource_1

lrm:Dependency

lrm:Resource_2

from

to

lrm:Resource_N

to

lrm:Description

lrm:Description

intention

specificationlrm:Plan

lrm:Plan

impact

precondition

lrm:Resource_Μ

from

lrm:Resourcelrm:Dependency

Ontology design patternshttp://ontologydesignpatterns.org/

Model

Digital video

Model captures expert knowledge about video based artworks

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

Video artwork model ◦ Codec, container, media player, operating system, computer◦ Can be extended to include additional components

Potential to incorporate additional factors◦ Display, software applications etc.

Bayesian modelling

Provides a dashboard to rapidly identify risks, proximities and scale

Collection and component-level views

Metadata extraction – e.g. MediaInfo for digital video

Determines potential recoverability options

Provides guidance rather than exact solutions

General purpose

Technical appraisal tool

Due for release Jan/Feb 2017

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 and potential recoverability options

Conclusions