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Lossy Dictionaries
 In ESA ’01: Proceedings of the 9th Annual European Symposium on Algorithms
, 2001
"... Bloom filtering is an important technique for space efficient storage of a conservative approximation of a set S. The set stored may have up to some specified number of false positive members, but all elements of S are included. In this paper we consider lossy dictionaries that are also allowed to h ..."
Abstract

Cited by 5 (2 self)
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Bloom filtering is an important technique for space efficient storage of a conservative approximation of a set S. The set stored may have up to some specified number of false positive members, but all elements of S are included. In this paper we consider lossy dictionaries that are also allowed to have false negatives, i.e., leave out elements of S. The aim is to maximize the weight of included keys within a given space constraint. This relaxation allows a very fast and simple data structure making almost optimal use of memory. Being more time efficient than Bloom filters, we believe our data structure to be well suited for replacing Bloom filters in some applications. Also, the fact that our data structure supports information associated to keys paves the way for new uses, as illustrated by an application in lossy image compression.
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"... Abstract. Bloom ltering is an important technique for space ecient storage of a conservative approximation of a set S. The set stored may have up to some specied number of false positive members, but all elements of S are included. In this paper we consider lossy dictionaries that are also allowed ..."
Abstract
 Add to MetaCart
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Abstract. Bloom ltering is an important technique for space ecient storage of a conservative approximation of a set S. The set stored may have up to some specied number of false positive members, but all elements of S are included. In this paper we consider lossy dictionaries that are also allowed to have false negatives. The aim is to maximize the weight of included keys within a given space constraint. This relaxation allows a very fast and simple data structure making almost optimal use of memory. Being more time ecient than Bloom lters, we believe our data structure to be well suited for replacing Bloom lters in some applications. Also, the fact that our data structure supports information associated to keys paves the way for new uses, as illustrated by an application in lossy image compression. 1