Introduction: Bloom filters [1] are a space-efficient, probabilistic data structure for representing a list of elements (for example, a list of strings). A Bloom filter is an array of m bits. A string is mapped into a Bloom filter by inputting it to a group of k hash functions resulting in k array positions. Each indexed array position is set to 1. A string is tested for membership by inputting...

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...