If you are interested in new cardinality estimation algorithms for HyperLogLog sketches you could also have a look on the paper I am currently working on:
http://oertl.github.io/hyperloglog-sketch-estimation-paper/
There, I present two different algorithms based on theoretical considerations that are both accurate over the entire cardinality range. Unlike LogLog-Beta or HyperLogLog++, they do not need any empirically determined coefficients or bias correction data.
Re:Cardinality Estimation (2011) [pdf]
by octernion on Thursday December 22, 2016 @02:30PM
(#13239651)
See the Arxiv paper[0] on it.
Found this from the Redis code[1] (I was curious, too).
[0]:https://arxiv.org/abs/1612.02284
[1]:https://github.com/antirez/redis/blob/87538cb7fe19b567118944...
Interesting, has anything been written about that? I've learned most of what I know about cardinality estimation from Redis and your writing on the matter, so I'd love to see more. It would be great to re-visit an article I wrote about HLL[0] from a new perspective as well.
[0]: https://blog.codeship.com/counting-distinct-values-with-hype...