Filters








2,179 Hits in 7.3 sec

The Power of 1 + α for Memory-Efficient Bloom Filters

Evgeni Krimer, Mattan Erez
2011 Internet Mathematics  
The algorithm relies on the power-of-two choice principle to provide a better distribution of set elements in a Blocked Bloom Filter.  ...  The paper also discusses an optimization technique to trade off cache effectiveness with the false-positive rate to fine-tune the Bloom Filter properties.  ...  Would also like to thank Isaac Keslassy for fruitful discussions at the early stages of this research and Mehmet Basoglu for his help with preparing this manuscript for publication.  ... 
doi:10.1080/15427951.2011.560785 fatcat:kfavurnnirgwpi6g6x5a7dpjeq

Using the Power of Two Choices to Improve Bloom Filters

Steve Lumetta, Michael Mitzenmacher
2007 Internet Mathematics  
We consider the combination of two ideas from the hashing literature: the power of two choices and Bloom filters.  ...  improved results for Bloom filters while using the same basic approach they employ, as opposed to designing new, more complex data structures for the problem.  ...  The first author was supported in part by NSF grant ACI-9984492. The second author was supported in part by NSF grant CCR-0121154 and a grant from Cisco.  ... 
doi:10.1080/15427951.2007.10129136 fatcat:6zdh46ks6jacdn65wl3fdk6q2y

Satisfiability-based Set Membership Filters

Sean A. Weaver, Katrina J. Ray, Victor W. Marek, Andrew J. Mayer, Alden K. Walker
2014 Journal on Satisfiability, Boolean Modeling and Computation  
As well, this is the first application that makes use of the random k-SAT phase transition results and may drive research into efficient solvers for this and similar applications.  ...  Specifically, a SAT filter is a filter construction that is simple yet efficient in terms of both query time and filter size; i.e., SAT filters asymptotically achieve the information-theoretic limit while  ...  Hsu, Donna Smith, and Christian Svetnicka for many engaging discussions on the subject of random k-SAT and set membership filters.  ... 
doi:10.3233/sat190095 fatcat:rnjjln3w3ne5xnyda7ii2iuz2a

Effect of Hash Function on Performance of Low Power Wake up Receiver for Wireless Sensor Network

Vikas Kumar, Rajender Kumar
2010 International journal of network security and its applications  
The design and implementation of a wireless wake-up receiver module simulation reveals that proposed model consume 724nW dynamic power and with bloom filter, the proposed model consumes dynamic power 85%  ...  less than the consumption cited in "Takiguchi" model [1] .  ...  in our network or not • We can also perform parallel processing for further optimising power • Give mismatch as 1st 0, obtain from memory BLOOM FILTER Two basic operations are defined for Bloom Filter  ... 
doi:10.5121/ijnsa.2010.2407 fatcat:o37dhg54trfzpchpgskqblfhm4

Multi-match Packet Classification Scheme Combining TCAM with an Algorithmic Approach

Hysook Lim, Nara Lee, Jungwon Lee
2017 IEIE Transactions on Smart Processing and Computing  
The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multimatch classifications.  ...  However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited.  ...  The required memory for the tuple Bloom filter is about 4 KB when the size of the Bloom filter is 8 ' p N , where 2 log ' 2 p N p N = .  ... 
doi:10.5573/ieiespc.2017.6.1.027 fatcat:f7fmcdw37fhk7nmqouyc7fogra

Performance-optimal filtering

Harald Lang, Thomas Neumann, Alfons Kemper, Peter Boncz
2019 Proceedings of the VLDB Endowment  
Besides the false-positive rate and memory footprint of the filter, performance optimality has to take into account the absolute cost of the filter lookup as well as the saved work per lookup that filtering  ...  While the space-precision tradeoff of these filters has been well studied, we show how to pick a filter that maximizes the performance for a given workload.  ...  The false-positive probability of a Cuckoo filter is f cuckoo (α, l, b) = 111 2 l 2bα , with: α = l * n m (8) where l is the signature length in bits and α the load factor of the table.  ... 
doi:10.14778/3303753.3303757 fatcat:sl4qogf4ungqjcot2qa72aka2i

Access-efficient Balanced Bloom Filters

Yossi Kanizo, David Hay, Isaac Keslassy
2013 Computer Communications  
The implementation of Bloom Filters in network devices ideally has low memory access-rate and low false positive rate.  ...  On the other hand, Blocked Bloom Filters first hash elements to a single memory block, where they maintain a local Bloom Filter.  ...  Unfortunately, while efficient in memory space, Bloom Filters require significant number of memory accesses.  ... 
doi:10.1016/j.comcom.2012.11.007 fatcat:zppvlbg5qfdejh7cjspw6csys4

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier [article]

Zhenwei Dai, Anshumali Shrivastava
2019 arXiv   pre-print
We proposed new algorithms that generalize the learned Bloom filter by using the complete spectrum of the scores regions.  ...  We proved our algorithms have lower False Positive Rate (FPR) and memory usage compared with the existing approaches to learned Bloom filter.  ...  We denote K as the number of hash functions used in the Bloom filter. 2 Review: Bloom Filter and Learned Bloom Filter Bloom Filter: Standard Bloom filter for compressing a set S consists of an R-bits array  ... 
arXiv:1910.09131v1 fatcat:6m4jkvxwpvfzlo2injttbpe4re

Scalable data center multicast using multi-class Bloom Filter

Dan Li, Henggang Cui, Yan Hu, Yong Xia, Xin Wang
2011 2011 19th IEEE International Conference on Network Protocols  
Specifically, MBF sets the number of hash functions in a perelement level, based on the probability for each Multicast group to be inserted into the Bloom Filter.  ...  Bloom Filter is an efficient tool to compress the Multicast forwarding table, but significant traffic leakage may occur when group membership testing is false positive.  ...  Efforts have been made on efficiently allocating the memory for a dynamic Bloom Filter, the size of which can vary.  ... 
doi:10.1109/icnp.2011.6089061 dblp:conf/icnp/LiCHXW11 fatcat:d2mbgxri4zbi7bxd6cp7zcc6y4

Efficient Histogram Estimation for Smart Grid Data Processing With the Loglog-Bloom-Filter

Yanjun Yao, Sisi Xiong, Hairong Qi, Yilu Liu, Leon M. Tolbert, Qing Cao
2015 IEEE Transactions on Smart Grid  
In this paper, we describe a novel data structure and its associated algorithms, called the loglog bloom filter, for this purpose.  ...  The primary challenge for achieving this goal using conventional methods is that keeping an individual counter for each unique type of data is too memory-consuming, slow, and costly.  ...  Markham for his valuable and insightful suggestions during the writing of this paper.  ... 
doi:10.1109/tsg.2014.2343997 fatcat:5b6ytqyfwrbpzgnxppg66alno4

Compressed bloom filters

Michael Mitzenmacher
2001 Proceedings of the twentieth annual ACM symposium on Principles of distributed computing - PODC '01  
The cost is the processing time for compression and decompression, which can use simple arithmetic coding, and more memory use at the proxies, which utilize the larger uncompressed form of the Bloom filter  ...  A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries.  ...  Acknowledgments The author would like to thank Andrei Broder for introducing him to Bloom filters and for helpful discussions.  ... 
doi:10.1145/383962.384004 dblp:conf/podc/Mitzenmacher01 fatcat:6nrj32wxrjd37hv6obwf5lpkkm

Compressed Bloom filters

M. Mitzenmacher
2002 IEEE/ACM Transactions on Networking  
The cost is the processing time for compression and decompression, which can use simple arithmetic coding, and more memory use at the proxies, which utilize the larger uncompressed form of the Bloom filter  ...  A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries.  ...  Acknowledgments The author would like to thank Andrei Broder for introducing him to Bloom filters and for helpful discussions.  ... 
doi:10.1109/tnet.2002.803864 fatcat:e5p2klmw65gkbie6qmgkkaptie

Longest prefix matching using bloom filters

S. Dharmapurikar, P. Krishnamurthy, D.E. Taylor
2006 IEEE/ACM Transactions on Networking  
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 efficient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted by prefix length  ...  The expression for the expected number of hash probes per lookup becomes Eexp = 2 × 1 2 M ln 2 α 24 N 24 +α 32 N 32 + 1 (16) Using the observed average distribution of prefixes and observed average values  ... 
doi:10.1109/tnet.2006.872576 fatcat:5pvcofat5fhe7ntbetofg7srem

Longest prefix matching using bloom filters

Sarang Dharmapurikar, Praveen Krishnamurthy, David E. Taylor
2003 Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications - SIGCOMM '03  
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 efficient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted by prefix length  ...  The expression for the expected number of hash probes per lookup becomes Eexp = 2 × 1 2 M ln 2 α 24 N 24 +α 32 N 32 + 1 (16) Using the observed average distribution of prefixes and observed average values  ... 
doi:10.1145/863977.863979 fatcat:aejhogwrtvefde56f4gfplfsni

Longest prefix matching using bloom filters

Sarang Dharmapurikar, Praveen Krishnamurthy, David E. Taylor
2003 Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications - SIGCOMM '03  
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 efficient data structure for membership queries, in order to determine address prefix membership in sets of prefixes sorted by prefix length  ...  The expression for the expected number of hash probes per lookup becomes Eexp = 2 × 1 2 M ln 2 α 24 N 24 +α 32 N 32 + 1 (16) Using the observed average distribution of prefixes and observed average values  ... 
doi:10.1145/863955.863979 dblp:conf/sigcomm/DharmapurikarKT03 fatcat:7kateructzbhzdrmd7q2avjxpe
« Previous Showing results 1 — 15 out of 2,179 results