The deletable Bloom filter: a new member of the Bloom family

Rothenberg, Christian Esteve; Macapuna, Carlos Alberto Bráz; Verdi, Fábio L.; Magalhães, Mauricio Ferreira

doi:10.1109/lcomm.2010.06.100344

Cited by 65 publications

(29 citation statements)

References 9 publications

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“…The Deletable Bloom filter (DlBF) [30] addresses the issue of enabling element deletions at a minimal cost in memorycompared to previous variants like the CBFs -and without introducing false negatives. The DlBF is based on a simple yet powerful idea, namely keeping record of the bit regions where collisions happen and exploiting the notion that elements can be effectively removed if at least one of its bits is reset.…”

Section: Deletable Bloom Filtermentioning

confidence: 99%

Theory and Practice of Bloom Filters for Distributed Systems

Tarkoma

Rothenberg

Lagerspetz

2012

IEEE Commun. Surv. Tutorials

443

244

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Abstract-Many network solutions and overlay networks utilize probabilistic techniques to reduce information processing and networking costs. This survey article presents a number of frequently used and useful probabilistic techniques. Bloom filters and their variants are of prime importance, and they are heavily used in various distributed systems. This has been reflected in recent research and many new algorithms have been proposed for distributed systems that are either directly or indirectly based on Bloom filters. In this survey, we give an overview of the basic and advanced techniques, reviewing over 20 variants and discussing their application in distributed systems, in particular for caching, peer-to-peer systems, routing and forwarding, and measurement data summarization.

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Section: Deletable Bloom Filtermentioning

confidence: 99%

Theory and Practice of Bloom Filters for Distributed Systems

Tarkoma

Rothenberg

Lagerspetz

2012

IEEE Commun. Surv. Tutorials

443

244

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“…the Bloom filter indicates that some absent elements are present) exists. However, the probability of false positive can be sufficiently reduced by either adjusting the number of hash functions k and the Bloom filter size s or some advanced techniques [14], [15]. We will investigate the effect of the false positive probability in Section 6.…”

Section: Timer-based Bloom Filter Aggregationmentioning

confidence: 99%

Timer-Based Bloom Filter Aggregation for Reducing Signaling Overhead in Distributed Mobility Management

Lee

Pack

et al. 2016

IEEE Trans. on Mobile Comput.

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Distributed mobility management (DMM) is a promising technology to address the mobile data traffic explosion problem. Since the location information of mobile nodes (MNs) are distributed in several mobility agents (MAs), DMM requires an additional mechanism to share the location information of MNs between MAs. In the literature, multicast or distributed hash table (DHT)-based sharing methods have been suggested; however they incur significant signaling overhead owing to unnecessary location information updates under frequent handovers. To reduce the signaling overhead, we propose a timer-based Bloom filter aggregation (TBFA) scheme for distributing the location information. In the TBFA scheme, the location information of MNs is maintained by Bloom filters at each MA. Also, since the propagation of the whole Bloom filter for every MN movement leads to high signaling overhead, each MA only propagates changed indexes in the Bloom filter when a pre-defined timer expires. To verify the performance of the TBFA scheme, we develop analytical models on the signaling overhead and the latency and devise an algorithm to select an appropriate timer value. Extensive simulation results are given to show the accuracy of analytical models and effectiveness of the TBFA scheme over the existing DMM scheme.

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“…Simple filters [3,16,7] only allow items to be inserted, and generally represent static sets. Deletable filters [12,14,15] allow items to be deleted as well as inserted, and represent dynamic sets. Decaying filters represent a dynamic set of recently inserted items.…”

Section: Related Workmentioning

confidence: 99%

Inferential time-decaying Bloom filters

Dautrich

Ravishankar

2013

Proceedings of the 16th International Conference on Extending Database Technology

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Time-Decaying Bloom Filters are efficient, probabilistic data structures used to answer queries on recently inserted items. As new items are inserted, memory of older items decays. Incorrect query responses incur penalties borne by the application using the filter. Most existing filters may only be tuned to static penalties, and they ignore Bayesian priors and information latent in the filter.We address these issues in an integrated way by converting existing filters into inferential filters. Inferential filters combine latent filter information with Bayesian priors to make query-specific optimal decisions. Our methods are applicable to any Bloom Filter, but we focus on developing inferential time-decaying filters, which support new query types and sliding window queries with varying error penalties.We develop the inferential version of the existing Timing Bloom Filter. Through experiments on real and synthetic datasets, we show that when penalties are query-specific and prior probabilities are known, the inferential Timing Bloom Filter reduces penalties for incorrect responses to slidingwindow queries by up to 70%.

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The deletable Bloom filter: a new member of the Bloom family

Cited by 65 publications

References 9 publications

Theory and Practice of Bloom Filters for Distributed Systems

Theory and Practice of Bloom Filters for Distributed Systems

Timer-Based Bloom Filter Aggregation for Reducing Signaling Overhead in Distributed Mobility Management

Inferential time-decaying Bloom filters

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