Scalable Bloom Filters

Almeida, Paulo Sérgio; Baquero, Carlos; Preguiça, Nuno; Hutchison, David

doi:10.1016/j.ipl.2006.10.007

Cited by 171 publications

(118 citation statements)

References 11 publications

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“…Almeida et al [2007] addressed this issue by using not one, but a series of Bloom filters of geometrically-increasing sizes.…”

Section: Expandable Bloom Filtersmentioning

confidence: 99%

“…One important difference that distinguishes our Bloom filter chains from the work of Almeida et al [2007] is in deciding which Bloom filter to probe. They require probing all the Bloom filters, but in our case, since elements are monotonically increasing, each Bloom filter in the chain contains a non-overlapping range of elements.…”

Section: Element Insertions and Probesmentioning

confidence: 99%

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Fast candidate generation for real-time tweet search with bloom filter chains

Asadi

Lin

2013

ACM Trans. Inf. Syst.

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The rise of social media and other forms of user-generated content have created the demand for real-time search: against a high-velocity stream of incoming documents, users desire a list of relevant results at the time the query is issued. In the context of real-time search on tweets, this work explores candidate generation in a two-stage retrieval architecture where an initial list of results is processed by a second-stage rescorer to produce the final output. We introduce Bloom filter chains, a novel extension of Bloom filters that can dynamically expand to efficiently represent an arbitrarily long and growing list of monotonically-increasing integers with a constant false positive rate. Using a collection of Bloom filter chains, a novel approximate candidate generation algorithm called BWAND is able to perform both conjunctive and disjunctive retrieval. Experiments show that our algorithm is many times faster than competitive baselines and that this increased performance does not require sacrificing end-to-end effectiveness. Our results empirically characterize the trade-off space defined by output quality, query evaluation speed, and memory footprint for this particular search architecture.

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“…Almeida et al [2007] addressed this issue by using not one, but a series of Bloom filters of geometrically-increasing sizes.…”

Section: Expandable Bloom Filtersmentioning

confidence: 99%

Section: Element Insertions and Probesmentioning

confidence: 99%

Fast candidate generation for real-time tweet search with bloom filter chains

Asadi

Lin

2013

ACM Trans. Inf. Syst.

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“…In our model we use standard bloom filters for simplicity and explanation, yet these drawbacks can be addressed by other bloom filter approaches. On the one hand, dynamic datasets are possible to model by using dynamic/scalable bloom filters [29], [30]. The main idea is that a scalable bloom filter is formed by one or more bloom filters.…”

Section: E Shortcomings and Solutionsmentioning

confidence: 99%

An item/user representation for recommender systems based on bloom filters

Pozo

Chiky

Meziane

et al. 2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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“…Instead of a set, a Bloom filter can be used for Pj, leading to lossy piggybacking. Scalable Bloom filters [1] can be used to maintain low the error rate. A different Bloom filter can be used for each acceptance group, so it suffices to store in the Bloom filter Pj only the projection si of (si, qj) on T .…”

Section: Multi-piggybackingmentioning

confidence: 99%

An improvement of the piggyback algorithm for parallel model checking

Filippidis

Holzmann

2014

Proceedings of the 2014 International SPIN Symposium on Model Checking of Software

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This paper extends the piggyback algorithm to enlarge the set of liveness properties it can verify. Its extension is motivated by an attempt to express in logic the counterexamples it can detect and relate them to bounded liveness. The original algorithm is based on parallel breadth-first search and piggybacking of accepting states that are deleted after counting a fixed number of transitions. The main improvement is obtained by renewing the counter of transitions when the same accepting states are visited in the negated property automaton. In addition, we describe piggybacking of multiple states in either sets (exact) or Bloom filters (lossy but conservative), and use of local searches that attempt to connect cycles fragmented among processing cores. Finally it is proved that accepting cycle detection is in NC in the size of the product automaton's entire state space, including unreachable states.

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Scalable Bloom Filters

Cited by 171 publications

References 11 publications

Fast candidate generation for real-time tweet search with bloom filter chains

Fast candidate generation for real-time tweet search with bloom filter chains

An item/user representation for recommender systems based on bloom filters

An improvement of the piggyback algorithm for parallel model checking

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