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Using Precomputed Bloom Filters to Speed Up SPARQL Processing in the Cloud
"... Increasingly data on the Web is stored in the form of Semantic Web data. Because of today’s information overload, it becomes very important to store and query these big datasets in a scalable way and hence in a distributed fashion. Cloud Computing offers such a distributed environment with dynamic ..."
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during query processing as it has been done traditionally, we precompute the bloom filters as much as possible and store them in the indices besides the data. The experimental results with data sets up to 1 billion triples show that our approach speeds up query processing significantly and sometimes even
Network Applications of Bloom Filters: A Survey
 INTERNET MATHEMATICS
, 2002
"... A Bloomfilter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used in ..."
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Cited by 522 (17 self)
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A Bloomfilter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used
Compressed Bloom Filters
, 2001
"... A Bloom filter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this drawback when the probability of an error is sufficiently low. We in ..."
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Cited by 255 (8 self)
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A Bloom filter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this drawback when the probability of an error is sufficiently low. We
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its ..."
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Cited by 417 (4 self)
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approaches in that it precomputes a kdtree data structure for the data points rather than the center points. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a datasensitive analysis of the algorithm's running time. Second, we have implemented
Deep Packet Inspection Using Parallel Bloom Filters
, 2004
"... this memory core, five randommemory locations are readable in a single clock cycle. So performing 35 concurrent memory operations requires seven parallel memory cores, each with oneseventh of the required array size, as Figure 5b illustrates. Because the basic Bloom filter allows any hash function ..."
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Cited by 224 (18 self)
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this memory core, five randommemory locations are readable in a single clock cycle. So performing 35 concurrent memory operations requires seven parallel memory cores, each with oneseventh of the required array size, as Figure 5b illustrates. Because the basic Bloom filter allows any hash
Longest Prefix Matching using Bloom Filters
, 2003
"... We introduce the first algorithm that we are aware of to employ Bloom filters for Longest Prefix Matching (LPM). The algorithm performs parallel queries on Bloom filters, an e#cient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted by p ..."
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Cited by 111 (7 self)
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We introduce the first algorithm that we are aware of to employ Bloom filters for Longest Prefix Matching (LPM). The algorithm performs parallel queries on Bloom filters, an e#cient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted
Bloom Filter � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � , � � � � � � � � � � � � � � � � � � � � � � � � � �
"... [11,12] � � � � � � � � � � � � � � � � [13] � � � � � � � � [14] � � � � � � � � � � [7−9]. � � � �,Bloom Filter � � Hash � � � � � � � � � � � � � � � � � � � � � � ..."
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[11,12] � � � � � � � � � � � � � � � � [13] � � � � � � � � [14] � � � � � � � � � � [7−9]. � � � �,Bloom Filter � � Hash � � � � � � � � � � � � � � � � � � � � � �
On bloom filters
, 2006
"... Bloom filters are heavily used in the literature for efficiently representing sets with elements from a large universe. However, the current literature lacks some important functionality on bloom filters which can be proven useful in several application domains, especially in distributed and P2P sys ..."
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Cited by 1 (1 self)
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Bloom filters are heavily used in the literature for efficiently representing sets with elements from a large universe. However, the current literature lacks some important functionality on bloom filters which can be proven useful in several application domains, especially in distributed and P2P
An Optimal Bloom Filter Replacement
, 2004
"... This paper considers spaceefficient data structures for storing an approximation S ′ to a set S such that S ⊆ S′ and any element not in S belongs to S ′ with probability at most . The Bloom filter data structure, solving this problem, has found widespread use. Our main result is a new RAM data stru ..."
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Cited by 48 (3 self)
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This paper considers spaceefficient data structures for storing an approximation S ′ to a set S such that S ⊆ S′ and any element not in S belongs to S ′ with probability at most . The Bloom filter data structure, solving this problem, has found widespread use. Our main result is a new RAM data
An Improved Construction for Counting Bloom Filters
 14th Annual European Symposium on Algorithms, LNCS 4168
, 2006
"... Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. As with a Bloom filter, a CBF obtains space savings by allowing false positives. We provide a simple hashingbas ..."
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Cited by 69 (5 self)
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Abstract. A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. As with a Bloom filter, a CBF obtains space savings by allowing false positives. We provide a simple hashing
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