  In this paper we compare two probabilistic data structures for association
queries derived from the well-known Bloom filter: the shifting Bloom filter
(ShBF), and the spatial Bloom filter (SBF). With respect to the original data
structure, both variants add the ability to store multiple subsets in the same
filter, using different strategies. We analyse the performance of the two data
structures with respect to false positive probability, and the inter-set error
probability (the probability for an element in the set of being recognised as
belonging to the wrong subset). As part of our analysis, we extended the
functionality of the shifting Bloom filter, optimising the filter for any
non-trivial number of subsets. We propose a new generalised ShBF definition
with applications outside of our specific domain, and present new probability
formulas. Results of the comparison show that the ShBF provides better space
efficiency, but at a significantly higher computational cost than the SBF.
