Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to model the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, clarifying what guarantees can and cannot be associated with such a structure.

Article history: Received 16 December 2009 Received in revised form 9 July 2010 Accepted 23 July 2010 Available online 3 August 2010 Communicated by S.E. Hambrusch

this experiment was conducted to consider the effects of maturity and cutting time on chemical composition and available protein content of alfalfa hay for dairy cows.. alfalfa hay harvested at two maturity stages (pre-bloom and early bloom) and two cutting times (morning and afternoon). available protein was predicted using nrc (2001) and dve/oeb (1994) models. in the nrc model (2001), alfalfa...

In this paper, we propose a privacy-preserving variant of Bloom-filters. The Bloom-filter has many applications such as hash-based IP-traceback systems and Web cache sharing. In some of those applications, equipping the Bloom-filter with the privacy-preserving mechanism is crucial for the deployment. In this paper, we propose a cryptographically secure privacy-preserving Bloom-filter protocol. ...

Bloom filters are widely used although not always optimal. Notably in application areas where false negatives are bearable, other techniques can clearly be better. This master thesis shows that at least for a specific area in the parameter space Bloom filters are significantly outperformed even by trivial methods. We provide an analysis, show that many application areas where Bloom filters are ...