research-article

Probabilistic data structures for big data analytics: : A comprehensive review

Authors:

Amritpal Singh,

Albert Y. ZomayaAuthors Info & Claims

Volume 188, Issue C

https://doi.org/10.1016/j.knosys.2019.104987

Published: 05 January 2020 Publication History

Abstract

An exponential increase in the data generation resources is widely observed in last decade, because of evolution in technologies such as-cloud computing, IoT, social networking, etc. This enormous and unlimited growth of data has led to a paradigm shift in storage and retrieval patterns from traditional data structures to Probabilistic Data Structures (PDS). PDS are a group of data structures that are extremely useful for Big data and streaming applications in order to avoid high-latency analytical processes. These data structures use hash functions to compactly represent a set of items in stream-based computing while providing approximations with error bounds so that well-formed approximations get built into data collections directly. Compared to traditional data structures, PDS use much less memory and constant time in processing complex queries. This paper provides a detailed discussion of various issues which are normally encountered in massive data sets such as-storage, retrieval, query,etc. Further, role of PDS in solving these issues is also discussed where these data structures are used as temporary accumulators in query processing. Several variants of existing PDS along with their application areas have also been explored which give a holistic view of domains where these data structures can be applied for efficient storage and retrieval of massive data sets. Mathematical proofs of various parameters considered in the PDS have also been discussed in the paper. Moreover, the relative comparison of various PDS with respect to various parameters is also explored.

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Index Terms

Probabilistic data structures for big data analytics: A comprehensive review
1. Information systems
  1. Data management systems
  2. Information storage systems
    1. Record storage systems
2. Theory of computation
  1. Design and analysis of algorithms
  2. Theory and algorithms for application domains
    1. Database theory
      1. Data structures and algorithms for data management

Index terms have been assigned to the content through auto-classification.

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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 188, Issue C

Jan 2020

541 pages

ISSN:0950-7051

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 05 January 2020

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Cited By

Kuszmaul WWalzer SMohar BShinkar IO'Donnell R(2024)Space Lower Bounds for Dynamic Filters and Value-Dynamic RetrievalProceedings of the 56th Annual ACM Symposium on Theory of Computing10.1145/3618260.3649649(1153-1164)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3618260.3649649
Kumar MSingh A(2024)Anomalous vehicle recognition in smart cities using persistent bloom filterTransactions on Emerging Telecommunications Technologies10.1002/ett.489635:1Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1002/ett.4896
Muhammad KObaidat MHussain TSer JKumar NTanveer MDoctor F(2021)Fuzzy Logic in Surveillance Big Video Data AnalysisACM Computing Surveys10.1145/344469354:3(1-33)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3444693

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