Bloofi: Multidimensional Bloom filters

Crainiceanu, Adina; Lemire, Daniel

doi:10.1016/j.is.2015.01.002

Cited by 37 publications

(25 citation statements)

References 27 publications

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“…The earliest use of Bloom filters as an index for a collection of independent documents we could find is called Bloofi by Crainiceanu and Lemire [11]. They propose to use a Bloom filter for each document and to arrange them either in a B-tree or as a Flat-Bloofi.…”

Section: Related Workmentioning

confidence: 99%

COBS: A Compact Bit-Sliced Signature Index

Bingmann

Bradley

Gauger

et al. 2019

Lecture Notes in Computer Science

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We present COBS, a COmpact Bit-sliced Signature index, which is a cross-over between an inverted index and Bloom filters. Our target application is to index k-mers of DNA samples or q-grams from text documents and process approximate pattern matching queries on the corpus with a user-chosen coverage threshold. Query results may contain a number of false positives which decreases exponentially with the query length. We compare COBS to seven other index software packages on 100 000 microbial DNA samples. COBS' compact but simple data structure outperforms the other indexes in construction time and query performance with Mantis by Pandey et al. in second place. However, unlike Mantis and other previous work, COBS does not need the complete index in RAM and is thus designed to scale to larger document sets.

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Section: Related Workmentioning

confidence: 99%

COBS: A Compact Bit-Sliced Signature Index

Bingmann

Bradley

Gauger

et al. 2019

Lecture Notes in Computer Science

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“…In addition to these studies that use an index, solutions using bloom filter have been proposed for the membership queries, where false positive matches are possible, but false negatives are not. 47,48 A query returns either possibly in a set or definitely not in the set. In other words, the bloom filter approach provides an approximate answer for a membership query with a reasonable performance.…”

Section: Related Workmentioning

confidence: 99%

Parallel membership queries on very large scientific data sets using bitmap indexes

Yildiz

Byna

et al. 2019

Concurrency and Computation

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Summary Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating‐point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word‐Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

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“…However, maintaining trie data structure is costly in terms of space as well as time. On the other hand, Bloofi [11] uses tree structured Bloom Filter which cuases costly in insertion and lookup. The scalability of BloomFlash [8], FBF [7], BloomStore [9], scaleBF is higher than TB 2 F and Bloofi [11].…”

Section: B Contributionmentioning

confidence: 99%

scaleBF: A High Scalable Membership Filter using 3D Bloom Filter

Patgiri¹,

Nayak²,

Borgohain³

2018

ijacsa

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Bloom Filter is extensively deployed data structure in various applications and research domain since its inception. Bloom Filter is able to reduce the space consumption in an order of magnitude. Thus, Bloom Filter is used to keep information of a very large scale data. There are numerous variants of Bloom Filters available, however, scalability is a serious dilemma of Bloom Filter for years. To solve this dilemma, there are also diverse variants of Bloom Filter. However, the time complexity and space complexity become the key issue again. In this paper, we present a novel Bloom Filter to address the scalability issue without compromising the performance, called scaleBF. scaleBF deploys many 3D Bloom Filter to filter the set of items. In this paper, we theoretically compare the contemporary Bloom Filter for scalability and scaleBF outperforms in terms of time complexity.

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Bloofi: Multidimensional Bloom filters

Cited by 37 publications

References 27 publications

COBS: A Compact Bit-Sliced Signature Index

COBS: A Compact Bit-Sliced Signature Index

Parallel membership queries on very large scientific data sets using bitmap indexes

scaleBF: A High Scalable Membership Filter using 3D Bloom Filter

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