The document discusses challenges around long-term knowledge retention in engineering. It references the story of "Funes the Memorious" who remembers every detail but lacks the ability to abstract and generalize. The document notes that long-term knowledge retention requires abstraction across domains and over time as concepts evolve. It also discusses the role of the Digital Curation Centre in developing best practices and tools to support long-term curation and preservation of digital assets.
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Issues in long-term knowledge retention in engineering
1. a centre of expertise in data curation and preservation
“Funes the Memorious”
Issues in long term knowledge
retention in engineering
Chris Rusbridge
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2. a centre of expertise in data curation and preservation
Contents
• DCC
• Last year?
• Funes, and the cobbler
• Knowledge, engineering, information,
longevity
• Next…
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Mission
“The over-riding purpose of the DCC is to
support and promote continuing improvement
in the quality of data curation, and of
associated digital preservation”
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Goals
• Strategic leadership in digital curation
• Influence and inform policy
• Provide advocacy and expert advice
• Provide resources and tools
• Raise levels of awareness and expertise
• Strengthen the community
• Continue links with leading edge research
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5. a centre of expertise in data curation and preservation
Status of DCC
• Phase 2 funded 3/2007 to 2/2010
• Associated project DCC SCARP to 4/2009
• Includes immersive case study approach including
engineering/architecture at Bath
• Just starting!
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Last year at LTKR…
• Established importance of problem
• Critical standards exist (STEP, OAIS)…
helpful but not sufficient?
• Identified some issues & limitations
• Wanted engineering information in (format)
registries
• Identified some archiving problems
• Listed some requirements
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7. a centre of expertise in data curation and preservation
“Funes the Memorious”
• Story by Jorge Luis Borges [1]
• Funes remembers everything
• 70,000 memories a day… how to classify?
• Puzzled why “the dog at 3:14 (seen from the side)
should have the same name as the dog at 3:15
(seen from the front)”
• “I suspect, however, that he was not very capable
of thought. To think is to forget differences,
generalize, make abstractions. In the teeming
world of Funes, there were only details, almost
immediate in their presence.”
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The cobbler?
•People
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
•Organisation
•Knowledge •Processes
•All in one person!
•Un-scalable, and mortal
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Abstraction
and domain
areas
•Peak, Lubell,
•Srinivasan &
•Waterbury,
•JCISE 2004 [2]
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Scalability: bring on the “M” names?
• ILM?
• Digital curation? No, 3rd party storage management
• KM?
• Integrate processes and agents through shared knowledge?
• With added requirements for long-term?
• “death/autism of KM?”
• Cultural change barriers: knowledge sharing still…
• Person to person
• Just in time (if you’re lucky)
• Problem-oriented
• PLM?
• Proprietary 3rd party? “Trust me…”
• Standards-based…
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What’s going on?
•Economic period •Compliance period
•Product database •archival
•Maintain •Retain
•Design •Build
•during life •After life
•Product information repository
•Completeness, accuracy, •+ provenance,
•versions, process, efficiency •longevity
•Continuing interpretability
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Archive/repository
• Preserves “information” (OAIS)
• For a Designated Community
• Receives Submission Information Packages
(SIPs)
• Negotiated
• Data in files
• Metadata
• Context & provenance, etc
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Designated Community
• An identified group of potential Consumers who
should be able to “understand” a particular set of
information
• Here: future engineers (and accident investigators!)
• Will 22nd century engineering concept set &
vocabulary be superset of today’s?
• No! Gradual concept drift…
• But likely discoverable by determined investigator.
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Slippery words
• “Polysemantic concepts such as information can be
fruitfully analysed only in relation to well-specified
contexts of application” Floridi [3]
• Understand
• Knowledge
• Knowing that, knowing how
• Information
• Factual, instructional, process
• Data
• “difference that makes a difference”
• Files
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Files, databases, packets
• No naked data
• In any independent existence the data are always
packaged in some way
• Encoded serialisation of data representing
information linked to an information model and/or
conceptual schema?
• Packaging adds extraneous “information”
• Confuses the archivist! (Most digital preservation
discussions relate to preserving “files” or at best
“digital objects”)
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ISO 19101 model
•ISO 19101
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Information
• “Standard definition of information” Floridi
• Comprises data that
• … is well-formed
• … and meaningful
• OAIS definition
• “Any type of knowledge that can be exchanged. In
an exchange, it is represented by data.”
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Representation information
• Heavy load!
• “Representation Information accompanying a digital
object, or sequence of bits, is used to provide
additional meaning. It typically maps the bits into
commonly recognized data types such as character,
integer, and real and into groups of these data types.
It associates these with higher-level meanings that
can have complex inter-relationships that are also
described.”
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Interpreted using…
• Today: software tools
• (although OAIS implies RepInfo often a text description…)
• Tomorrow:
• Same software running on a VM (emulation)?
• Different software specially built using the RepInfo?
• Maybe tool to “interpret” RepInfo eg in EAST?
• Maybe new software implementing legacy forms?
• Maybe transform old data for new software (migration)?
• Outcomes enhanced if information in widely-used
standards-based form!
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OAIS a Reference Model…
• Not a design
• Not an architecture
• There need be no element labeled RepInfo
• … but the CONCEPT of RepInfo should exist!
• Ie explicit relationship between data/files and
information according to a recorded
information model
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Engineering RepInfo?
• Do you have an underlying engineering information
model?
• What would representation information be for
engineering information (say encoded in STEP)?
• Definitions of appropriate STEP elements (eg the standard)
• Additional refinements added by the tools used
• +???
• What about what cannot be encoded in STEP?
• Or… what information would an engineer need who
had never used STEP or your analysis tools?
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Final questions
• What else makes your encoded product
information fragile?
• What can you do to make it more robust?
• What further tools, techniques, training and
infrastructures do you need to do a better
job?
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References
• [1] Borges, Jorge Luis. (1962) Labyrinths, Selected
Stories & Other Writings. Penguin Books
• [2] Peak, R., Lubell, J., Srinivasan, V., Waterbury, S.
(2004).
STEP, XML, and UML: Complementary Technologies.
Journal of Computing and Information Science in
Engineering 4 (4), 379-90
• [3] Floridi, L. (2005). Is Semantic Information
Meaningful Data? Philosophy and Phenomenological
Research, Vol. LXX, No. 2
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Editor's Notes
STEP, XML, and UML: Complementary Technologies1 Russell S. Peak, Georgia Institute of Technology Manufacturing Research Center Joshua Lubell, National Institute of Standards and Technology Vijay Srinivasan, IBM Corporation ASME Member Stephen C. Waterbury NASA Goddard Space Flight Center Submission for JCISE Special Issue: From Engineering Information Management to Product Lifecycle Management.
LUCIANO FLORIDI, Wolfson College, Oxford University Is Semantic Information Meaningful Data? Philosophy and Phenomenological Research, Vol. LXX, No. 2, March 2005