Fast and deterministic hash table lookup using discriminative bloom filters

Huang, Kun; Xie, Gaogang; Li, Rui; Xiong, Shuai

doi:10.1016/j.jnca.2012.12.031

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…A functional Bloom filter satisfies the key-value operation required in various applications [20][21][22][23][24][25]. Unlike a hash table, a functional Bloom filter does not require to store input keys, as different combinations of Bloom filter indexes for each input key can work as the signature of the input key.…”

Section: Introductionmentioning

confidence: 99%

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Byun

Lim

2020

Applied Sciences

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Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.

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Section: Introductionmentioning

confidence: 99%

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Byun

Lim

2020

Applied Sciences

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“…Besides state symbols, multi-bit words were also employed as an auxiliary array, where membership could be determined in only a small number of memory accesses [21]. Taking reducing memory access into account, a collision-free hashing scheme was developed on discriminative Bloom filters to speed up the lookup process [20]. A combinatorial Bloom filter was proposed using multiple sets of hash functions to code an input element into a binary array as the group identity of the element.…”

Section: Introductionmentioning

confidence: 99%

A CMAC-based scheme for determining membership with classification of text strings

Tseng

Chen

2015

Neural Comput & Applic

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Membership determination of text strings has been an important procedure for analyzing textual data of a tremendous amount, especially when time is a crucial factor. Bloom filter has been a well-known approach for dealing with such a problem because of its succinct structure and simple determination procedure. As determination of membership with classification is becoming increasingly desirable, parallel Bloom filters are often implemented for facilitating the additional classification requirement. The parallel Bloom filters, however, tend to produce additional false-positive errors since membership determination must be performed on each of the parallel layers. We propose a scheme based on CMAC, a neural network mapping, which only requires a single-layer calculation to simultaneously obtain information of both the membership and classification. A hash function specifically designed for text strings is also proposed. The proposed scheme could effectively reduce false-positive errors by converging the range of membership acceptance to the minimum for each class during the neural network mapping. Simulation results show that the proposed scheme committed significantly less errors than the benchmark, parallel Bloom filters, with limited and identical memory usage at different classification levels.

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“…The name space of named data is undoubtedly much larger than that of IP addresses and the data names themselves can also get very complex [ [3] but at the cost of checking multiple locations in the table for a single lookup query. In order to solve this problem, it has been proposed to keep some info of the off-chip hash table in the on-chip memory to assist the locating process, as in [4]. However these techniques require additional space and calculation resources dedicated to maintain these info.…”

Section: Introductionmentioning

confidence: 99%

Summary-aided bloom filter for high-speed named data forwarding

Pei

2013

2013 IEEE 14th International Conference on High Performance Switching and Routing (HPSR)

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In a content centric network, packet forwarding is performed over data names instead of IP addresses. Since data names are order-of-magnitude larger in number and complexity, CAM-based or Trie-based techniques are not applicable any more. and new forwarding schemes are proposed to solve the problem. These schemes use hashing to store the large routing table for named data into relatively abundant off-chip memory, and use some on-chip Bloom filter to minimize expensive off-chip memory access by quickly screen out table lookup queries for unrecorded names. In this paper we propose to add a 'summary vector' to the on-chip Bloom filter that can help in constructing an efficient off-chip hash table for better storage and lookup performance: a dynamic collision-free hash table that only needsto read into only one routing record for any lookup queries.

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Fast and deterministic hash table lookup using discriminative bloom filters

Cited by 14 publications

References 32 publications

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

A CMAC-based scheme for determining membership with classification of text strings

Summary-aided bloom filter for high-speed named data forwarding

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