  Filter data structures are widely used in various areas of computer science
to answer approximate set-membership queries. In many applications, the data
grows dynamically, requiring their filters to expand along with the data that
they represent. However, existing methods for expanding filters cannot maintain
stable performance, memory footprint, and false positive rate at the same time.
We address this problem with Aleph Filter, which makes the following
contributions. (1) It supports all operations (insertions, queries, deletes,
etc.) in constant time, no matter how much the data grows. (2) Given any rough
estimate of how much the data will ultimately grow, Aleph Filter provides far
superior memory vs. false positive rate trade-offs, even if the estimate is off
by orders of magnitude.
