I am not as articulate as many on this list, and I got lost sometimes
in the meanings of some of the abstractions. But here is something I
wrote quickly to Ben earlier today that captures some of my concerns,
as one of the "users" Peter metions - not as well stated as I would
like but I hope the gist of the idea is there.
Everything is a feature.....
Hi Ben:
So I actually got a little time to think, and I never was very fast
and as an old fogey my brain is getting even slower, but....
In an abstract sense, it may make sense that everything is a
feature, but in a practical sense it will lead to a tools/data
mismatch that is not efficient, and therefore I do not feel is a
useful model.
For example, in statistics the tools that you use if the data are
iid are different than the tool you use if the data are a time
series, and are different than the tools you use if the data is
purely spatial, and these differ than from the tools for space-
time, and multivariate timeseries, and multivariate space-time
data. There is no one method works best under all situations. If
all I know is that I am getting back is a feature, I know very
little, and matching the feature to the tool will be difficult.
Same with visualization tools - some are best for certain types of
data, and no one is best for all types (GIS systems are crummy
with dealing with time, or time/depth). Having data models that
reflect the types of data that we have (or the types of data that
will be returned) allows for the proper match.
