Results 211 to 220 of about 12,077 (255)

Persistent Bloom Filter

Proceedings of the 2018 International Conference on Management of Data, 2018
Membership testing is the problem of testing whether an element is in a set of elements. Performing the test exactly is expensive space-wise, requiring the storage of all elements in a set. In many applications, an approximate testing that can be done quickly using small space is often desired.
Yanqing Peng, Jinwei Guo, Feifei Li, Weining Qian, Aoying Zhou   +4 more
openaire   +1 more source

Compacted Bloom Filter

2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC), 2016
A Bloom filter is a space-efficient probabilistic data structure that is used in many domains including networking applications to test for set memberships. Such applications often require sending Bloom filters using messages. Consequently, it is important to minimize the size of the filters such that the storage, transmission, and processing costs are
Negar Mosharraf, Anura P. Jayasumana, Indrakshi Ray   +2 more
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Weighted Bloom Filter

2006 IEEE International Symposium on Information Theory, 2006
A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information and has broad applications. In this paper, we generalize the traditional Bloom filter to Weighted Bloom Filter, which incorporates the ...
Jehoshua Bruck, Jie Gao, Anxiao Jiang
openaire   +1 more source

PFBF: Pre-Filtered Bloom Filters

2015
In this paper we focus on improving the false positive rate of a bloom filter with a pre-filtering scheme. By applying this scheme on a bloom filter, we can quickly screen out lots of input before entering the bloom filter and hence improve the result of false positives.
Liu Ssu-Ting, Wang Sheng-De
openaire   +1 more source

Cuckoo Bloom Filter

2019 International Conference on Electronics, Information, and Communication (ICEIC), 2019
A membership identification is a key functionality in many network applications. Various data structures have been introduced in order to support the efficient membership identification. Since a Bloom filter can provide simple but efficient membership checking, it is widely used in many network applications.
Ju Hyoung Mun, Hyesook Lim
openaire   +1 more source

MultiLayer Compressed Counting Bloom Filters

IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, 2008
Bloom filters are efficient randomized data structures for membership queries on a set with a certain known false positive probability. Counting bloom filters (CBFs) allow the same operation on dynamic sets that can be updated via insertions and deletions with larger memory requirements. This paper first presents a new upper bound for counters overflow
Ficara D, GIORDANO, STEFANO, PROCISSI, GREGORIO, VITUCCI, FABIO   +3 more
openaire   +1 more source

Complement Bloom Filter for Identifying True Positiveness of a Bloom Filter

IEEE Communications Letters, 2015
The use of Bloom filters in network applications has increased rapidly. Since Bloom filters can produce false positives, the trueness of each positive needs to be identified by referring to an off-chip hash table. This letter proposes a new method for identifying the trueness of Bloom filter positives.
Hyesook Lim, Jungwon Lee, Changhoon Yim
openaire   +1 more source

Dynamic reordering bloom filter

2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2017
In order to check a membership in multiple sets of bloom filter in a dynamic bloom filter, a sequential search is usually used. Since the distribution of queried data is unpredictable because the distribution has a feature of temporal locality. Therefore more search cost is incurred if queried data is stored in the peer which is corresponded to the ...
Da-Chung Chang, Chien Chen, Mahadevan Thanavel   +2 more
openaire   +1 more source

The Dynamic Bloom Filters

IEEE Transactions on Knowledge and Data Engineering, 2010
A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level.
Deke Guo, Jie Wu, Honghui Chen, Ye Yuan, Xueshan Luo   +4 more
openaire   +1 more source

One-hashing bloom filter

2015 IEEE 23rd International Symposium on Quality of Service (IWQoS), 2015
Bloom filters are widely used in many network applications but the high computation cost limits the system performance. In this paper, we introduce a new variation of Bloom filter named One-Hashing Bloom Filter (OHBF) to solve the problem. OHBF requires only one base hash function plus a few simple operations to implement a Bloom filter.
Jianyuan Lu, Tong Yang, Yi Wang, Huichen Dai, Linxiao Jin, Haoyu Song, Bin Liu   +6 more
openaire   +1 more source