Great to hear from you. I wondered how many of the old-timers were still around.
Why not use composite type ? For simple interval approach it's worked for us (see attached hdate.sql).
I have just begun looking at your hdate example; I see potentially useful stuff in it, but the first thing that I noticed is hat it is not fully equivalent to my problem. It looks like you only need to match intervals, while I need to match intervals and something else — ideally, in a single operation. I attempted to explain that in my reply to Craig Ringer.
If you need to specify distribution function,
Not in this case; there is no uncertainty associated with the loci; where there is uncertainty is in the existence of a feature called at a locus: is it real or is it a technogenic artifact? But that is a different problem for a later day.
I love uncertainty, and I’ve always wished I could make it computable. I also wish folks around me had the same appreciation for it. My job is to say yes or no where the data suggest maybe, or maybe not. Needless to say, I feel a bit exercised.
I am reading the info you provided with keen interest.
Great stuff, I was not aware of it. I saw it in early development but did not know it made it to the core. I tried it (and will go and update a few kludgy apps where I had to use bad surrogates). It is not directly applicable to genomic loci because it will require additional constraints for intelligent matching. I want to go for compete encapsulation of constraints.
Part of the reason for such a perverse desire is that I use the database as a calculator — that is, I load some data in a one-off experiment and I literally type everything in psql while I muddle through. There is a limit on how much I can type and not screw things up beyond comprehension, so I want the query language to be as easy and interactive as possible. Having to drag along a set of additional constraints is not quite interactive and is error-prone.