Abstract: A method for classification includes extracting respective classification keys from a collection of data items and defining a set of patterns for matching to the classification keys. A plurality of memory banks contain respective Bloom filters, each Bloom configured to indicate one or more patterns in the set that are candidates to match a given classification key. A respective first hash function is applied to the classification keys for each pattern in order to select, for each classification key, one of the Bloom filters to query for the pattern. The selected Bloom filters are queried by applying a respective second hash function to each classification key, so as to receive from the Bloom filters an indication of the one or more candidate patterns. The data items are classified by matching the respective classification keys against the candidate patterns.

Abstract: A method for classification includes extracting respective classification keys from a collection of data items and defining a set of patterns for matching to the classification keys. A plurality of memory banks contain respective Bloom filters, each Bloom configured to indicate one or more patterns in the set that are candidates to match a given classification key. A respective first hash function is applied to the classification keys for each pattern in order to select, for each classification key, one of the Bloom filters to query for the pattern. The selected Bloom filters are queried by applying a respective second hash function to each classification key, so as to receive from the Bloom filters an indication of the one or more candidate patterns. The data items are classified by matching the respective classification keys against the candidate patterns.