Big Data Analytics in Association Rule Mining: A Systematic Literature Review

Mahtab Shahin; Sijo Arakkal Peious; Rahul Sharma; Minakshi Kaushik; Sadok Ben Yahia; Syed Attique Shah; Dirk Draheim

doi:10.1145/3474944.3474951

Big Data Analytics in Association Rule Mining: A Systematic Literature Review

Mahtab Shahin^*
, Sijo Arakkal Peious
, Rahul Sharma
, Minakshi Kaushik
, Sadok Ben Yahia
, Syed Attique Shah
, Dirk Draheim

^*Kontaktforfatter

Tallinn University of Technology
Tallinn University of Technology
University of Tartu

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

Abstract

Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).

Originalsprog	Engelsk
Titel	BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology
Forlag	Association for Computing Machinery / Special Interest Group on Programming Languages
Publikationsdato	16. jan. 2021
Sider	40-49
ISBN (Elektronisk)	9781450389280
DOI	https://doi.org/10.1145/3474944.3474951
Status	Udgivet - 16. jan. 2021
Udgivet eksternt	Ja
Begivenhed	3rd International Conference on Big Data Engineering and Technology, BDET 2021 - Virtual, Online, Singapore Varighed: 25. jun. 2021 → 27. jun. 2021

Konference

Konference	3rd International Conference on Big Data Engineering and Technology, BDET 2021
Land/Område	Singapore
By	Virtual, Online
Periode	25/06/2021 → 27/06/2021

Navn	ACM International Conference Proceeding Series

Bibliografisk note

Publisher Copyright:
© 2021 ACM.

Dokumenter og links

10.1145/3474944.3474951

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

Shahin, M., Arakkal Peious, S., Sharma, R., Kaushik, M., Ben Yahia, S., Shah, S. A., & Draheim, D. (2021). Big Data Analytics in Association Rule Mining: A Systematic Literature Review. I BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology (s. 40-49). Association for Computing Machinery / Special Interest Group on Programming Languages. https://doi.org/10.1145/3474944.3474951

Shahin, Mahtab ; Arakkal Peious, Sijo ; Sharma, Rahul et al. / Big Data Analytics in Association Rule Mining : A Systematic Literature Review. BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology. Association for Computing Machinery / Special Interest Group on Programming Languages, 2021. s. 40-49 (ACM International Conference Proceeding Series).

@inproceedings{380a44f21dc1460d896c33cb510364d1,

title = "Big Data Analytics in Association Rule Mining: A Systematic Literature Review",

abstract = "Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).",

keywords = "Association rule mining, Big data analytics, MapReduce, Spark, systematic literature review",

author = "Mahtab Shahin and \{Arakkal Peious\}, Sijo and Rahul Sharma and Minakshi Kaushik and \{Ben Yahia\}, Sadok and Shah, \{Syed Attique\} and Dirk Draheim",

note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 3rd International Conference on Big Data Engineering and Technology, BDET 2021 ; Conference date: 25-06-2021 Through 27-06-2021",

year = "2021",

month = jan,

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doi = "10.1145/3474944.3474951",

language = "English",

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publisher = "Association for Computing Machinery / Special Interest Group on Programming Languages",

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Shahin, M, Arakkal Peious, S, Sharma, R, Kaushik, M, Ben Yahia, S, Shah, SA & Draheim, D 2021, Big Data Analytics in Association Rule Mining: A Systematic Literature Review. i BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology. Association for Computing Machinery / Special Interest Group on Programming Languages, ACM International Conference Proceeding Series, s. 40-49, 3rd International Conference on Big Data Engineering and Technology, BDET 2021, Virtual, Online, Singapore, 25/06/2021. https://doi.org/10.1145/3474944.3474951

Big Data Analytics in Association Rule Mining: A Systematic Literature Review. / Shahin, Mahtab; Arakkal Peious, Sijo; Sharma, Rahul et al.
BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology. Association for Computing Machinery / Special Interest Group on Programming Languages, 2021. s. 40-49 (ACM International Conference Proceeding Series).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

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T2 - 3rd International Conference on Big Data Engineering and Technology, BDET 2021

AU - Shahin, Mahtab

AU - Arakkal Peious, Sijo

AU - Sharma, Rahul

AU - Kaushik, Minakshi

AU - Ben Yahia, Sadok

AU - Shah, Syed Attique

AU - Draheim, Dirk

PY - 2021/1/16

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N2 - Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).

AB - Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).

KW - Association rule mining

KW - Big data analytics

KW - MapReduce

KW - Spark

KW - systematic literature review

U2 - 10.1145/3474944.3474951

DO - 10.1145/3474944.3474951

M3 - Article in proceedings

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PB - Association for Computing Machinery / Special Interest Group on Programming Languages

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Shahin M, Arakkal Peious S, Sharma R, Kaushik M, Ben Yahia S, Shah SA et al. Big Data Analytics in Association Rule Mining: A Systematic Literature Review. I BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology. Association for Computing Machinery / Special Interest Group on Programming Languages. 2021. s. 40-49. (ACM International Conference Proceeding Series). doi: 10.1145/3474944.3474951

Big Data Analytics in Association Rule Mining: A Systematic Literature Review

Abstract

Konference

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater