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

^*Corresponding author for this work

Tallin University of Technology
Tallinn University of Technology
University of Tartu

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › 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).

Original language	English
Title of host publication	BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology
Publisher	Association for Computing Machinery / Special Interest Group on Programming Languages
Publication date	16. Jan 2021
Pages	40-49
ISBN (Electronic)	9781450389280
DOIs	https://doi.org/10.1145/3474944.3474951
Publication status	Published - 16. Jan 2021
Externally published	Yes
Event	3rd International Conference on Big Data Engineering and Technology, BDET 2021 - Virtual, Online, Singapore Duration: 25. Jun 2021 → 27. Jun 2021

Conference

Conference	3rd International Conference on Big Data Engineering and Technology, BDET 2021
Country/Territory	Singapore
City	Virtual, Online
Period	25/06/2021 → 27/06/2021

Series	ACM International Conference Proceeding Series

Keywords

Association rule mining
Big data analytics
MapReduce
Spark
systematic literature review

Documents & Links

10.1145/3474944.3474951

SDU Link

Check access to the material in SDU Library's search engine

Cite this

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. In BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology (pp. 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. pp. 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,

day = "16",

doi = "10.1145/3474944.3474951",

language = "English",

series = "ACM International Conference Proceeding Series",

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

pages = "40--49",

booktitle = "BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology",

}

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. in 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, pp. 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. p. 40-49 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Big Data Analytics in Association Rule Mining

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

Y1 - 2021/1/16

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

AN - SCOPUS:85118187459

T3 - ACM International Conference Proceeding Series

SP - 40

EP - 49

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

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

Y2 - 25 June 2021 through 27 June 2021

ER -

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. In BDET 2021 - 2021 3rd International Conference on Big Data Engineering and Technology. Association for Computing Machinery / Special Interest Group on Programming Languages. 2021. p. 40-49. (ACM International Conference Proceeding Series). doi: 10.1145/3474944.3474951

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

Abstract

Conference

Keywords

Documents & Links

SDU Link

Fingerprint

Cite this