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Provenance use cases

From: Cory Casanave <cory-c@modeldriven.com>
Date: Wed, 20 Jan 2010 17:14:31 -0500
Message-ID: <4F65F8D37DEBFC459F5A7228E5052044A03443@DATCENTRALSRV.datcentral.local>
To: "Simon Miles" <simon.miles@kcl.ac.uk>, <public-xg-prov@w3.org>
Dear provenance incubator group,
I have been monitoring the group for a few weeks now and would like to
offer a couple of use cases into the mix.  These are both based from our
business and enterprise architecture work in the U.S. government by
model driven solutions (modeldriven.com).

Use case: Who said that?
The scenario is based on a financial architecture being done for a
government agency.  This is a large architecture involving information,
services and processes.  Most of the stakeholders are non-technical,
many accountants.  As with any such architecture it is based on a
successive set of inputs and meetings with stakeholders - not all at the
same time.  While this architecture was not being done with semweb
tooling (it was UML), the same situation arises despite the formalism
used.  Near the end of the project one of the stakeholders was reviewing
an information model for orders.  This was not the first time this
stakeholder had seen this part of the model, but they had not reviewed
it in some time.
The stakeholder pointed to a property on part of the model dealing with
orders and asked: "Where did that come from, who told you to put it
in?".  Certainly a reasonable question but one we could not answer
without a long dig through manual notes.  There was nothing in the model
to say where that property came from, when it was added or under what
authority.  In addition the stakeholder noted that something they though
was in the model had been removed and wanted to know where it had gone.
Again, the tooling could not help.
Conclusion: The source (both the person entering the data and who told
them to put it their), the situation (such as a meeting) and the time of
each assertion in the model needs to be tracked.  This should be part of
the core knowledge management infrastructure and leads directly to the
trustworthiness of the knowledge base as it evolves over time.

Use case: Cheating dictator
It seems that certain intelligence activities look at things like the
college transcripts of interesting people and use these to draw
conclusions about their capability and character.  The story (and it may
just be a story) is that Sadum Housane attended a college in Australia
decades ago.  The transcripts for that college were obtained and made
part of his personal profile.  This profile impacted important political
and military activities.
It became apparent that for propaganda purposes these transcripts had
been modified.  Analysts wanted to know what inferences had been made by
human and automated means, what information was inferred and how that
could change Sadum's profile and potential actions.  There was no way to
trace this information path, making many of the opinions questionable.
This is, of course, only one small example in the world where
information may be intentionally falsified or obscured and where the
resulting conclusions are critically important.  The source and
down-stream impact of information is critical, particularly when sources
and information quality are re-evaluated.
Conclusion: The track of inferences may span decades and this track may
be of critical strategic value.  In addition, inference is a combination
of human and automated activities that effect down-stream conclusions.  

I hope you find these use cases of interest.
Cory Casanave
Model Driven Solutions
Received on Wednesday, 20 January 2010 22:14:55 UTC

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