Provenance and pragmatics
Computer and database experts are doing much work to address provenance issues in
formal terms. Meanwhile organisations which collect, organise and serve data have
evolved pragmatic practices of differing levels of functionality. The aim of this breakout
group was to expose existing practice of databases and discuss it in the context of
database methods.
Participants from four different domains presented brief overviews of the practice
within their domain:
Engineering – the maintenance of list of components with different versions used in
electronic engineering products
Astronomy – data from sky surveys at different wavelengths and catalogues of objects
Environmental simulation – data generated by simulations of the marine environment
in a river mouth
Bioinformatics – shared data collections of biomolecular information.
Engineering
Astronomy
Simulation
Bioinformatics
Key driver for
provenance
Ensuring
correctness
Evoking
scientific trust
Ensuring
comparabilty of
data, liability
issues
Evoking trust,
data quality,
attribution
Key issues
Recording
“used in”
information to
allow updates
Data
transformation
from raw data
Tweaking of
details of
simulation
creates
incompatibilities
Assertions
based on whole
database
comparisons
Motivations for capturing provenance information
The disciplines under consideration identified a range of motivations for wishing to
collect provenance information. These were broadly shared, but the emphasis differed
from discipline to discipline.
Quality/Trust
Provenance information was important in enabling scientists to reasonably assess how
much trust they could put in particular information. This was particularly true in
astronomy. It was noted that “cheap” and easily reproduced information (e.g., DNA
sequence) is less jeopardised by poor provenance information as typically scientists will
typically resequence to check data crucial to their scientific direction. By contrast, there
is substantial concern about the provenance of expensive information (e.g.,
macromolecular structures) where repeating experiments is impractical, or for “onechance-to-collect” information (e.g., cosmic events).
Attribution
Correct attribution of scientific findings depends on the databases which include them
carrying provenance information. In the biological arena this is seen as important as
part of scientific credit, though it breaks down where composite objects are derived
from the work of hundreds of scientists. It seems less of an issue for large-scale genome
data or sky surveys, where the attribution is pretty clear. Liability (blame assignment)
was seen as the dark side of attribution.
Priority
In the biological world patent documentation searches may need to know exactly who
created what and when. At the EBI special systems to support this have been developed
(at the expense of the European Patent Office).
Interpretation
Provenance of the information can be important to ensuring its correct interpretation.
This requirement was particularly important in the environmental simulation data, where
one would like to draw conclusions by comparing periodic data generated over a long
time scale by evolving simulation methods.
Interoperability
Connections between different data collections depends on the ability to identify
equivalent (or related) objects.
Roll-back
Often the data presented are the result of refinement/reduction/transformation of
“raw” data. If this pre-processing turns out to be suspect it is necessary to re-examine or
recompute the data.
Cost/Benefit/Risk
It became clear that, in practice, data curation centres are either implicitly or explicitly
making cost/benefit decisions about the collection and propagation of provenance
information along with an intuitive assessment of the risks associated with failure to do
so. Thus:
Cheap, reproducible data carry little risk as they can be redetermined, and so there is
less pressure to record provenance
High-aggregation provenance (e.g., information derived from comparison with large,
changing databases) seems expensive to collect well, and its suppliers simply give up on
provenance.
Expensive data (e.g., sky-surveys, macromolecular structures) create pressure to
capture provenance because doing so is cheap by comparison with recollecting the data.
Unique event data (e.g., cosmic events) cannot be verified after the fact, so they are
only as good as the associated provenance information.
Life-critical data – (e.g., drug ingredient pedigree, aircraft component versions) create
pressure to capture provenance due to the high risk associated with not doing so. We
are therefore prepared to tolerate a fairly high cost in capturing the information.
Reducing the cost of provenance information
A defeatist might conclude that we effectively only create provenance information when
it’s essential or easy, and give up otherwise. However the group was of the view that
there were some obvious strategies to reduce the burden of collecting the information,
and was willing to accept that there might also be some non-obvious strategies.
Archive rather than serve provenance
Sometimes the need to access the provenance information may be rare and insufficient
to merit little more than securing it such that it can be reworked. For example, old
versions of sequence databases are typically held, but it is substantial work to retrieve
them.
Automate
If the tools which the data producers use along the way to creating their data record
what they do in a standard way then this information can be “harvested” to create high
quality provenance information with less effort.
…and the non-obvious
Can the task of recording the provenance of a complex and ever-changing database be
turned into a database engineering problem rather than a scientific domain problem.
Could the very technology used to maintain the database also maintain the provenance
information. The domain specialists look to the computer scientists for help here.