> It drives me insane to see papers that claim that some problem is "unsolved" when most of the industry players have already solved it, but they didn't happen to write an NSDI or SIGCOMM paper about it.
I've seen many examples of industry "solutions" that aren't documented, aren't published, and aren't even validated. There's a place for papers like these. I'm not quite your typical CS researcher (I do applied math and software for medical imaging), so YMMV, but I think this criticism is too harsh.
That's a fair point. The issue is that many of these papers don't seem to acknowledge that industry has (unpublished) solutions, and are somewhat naive as a result.
Another way to look at it: avoid the open/academic community stop doing xrypto research because NSA mathematicians are (secretly) way ahead of them?
"Open" is a different league from proprietary. It doesn't matter that they are behind proprietary. but it matters when they are working on the wrong problems.
It's unfortunate that, in science, if it isn't written down, it doesn't exist.
Having been bitten by the 'but we already know how to do that' comments, I find this particular aspect of industry to be very irritating. There are 3 possibilities:
The unpublished solution is brilliant.
The solution fits for very limited constraints applicable only to that situation.
The solution is a half-assed hack that only looks like it works.
In only one of those situations is the 'naive' comment valid.
Fair. This happens less in my field, but a lot of that could be because, for medical applications, people are far less willing to certify without published validation.
> It drives me insane to see papers that claim that some problem is "unsolved" when most of the industry players have already solved it, but they didn't happen to write an NSDI or SIGCOMM paper about it.
I've seen many examples of industry "solutions" that aren't documented, aren't published, and aren't even validated. There's a place for papers like these. I'm not quite your typical CS researcher (I do applied math and software for medical imaging), so YMMV, but I think this criticism is too harsh.