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

被引：10

作者：

Shahin, Mahtab ^{[1
]}

Peious, Sijo Arakkal ^{[1
]}

Sharma, Rahul ^{[1
]}

Kaushik, Minakshi ^{[1
]}

Ben Yahia, Sadok ^{[2
]}

Shah, Syed Attique ^{[3
]}

Draheim, Dirk ^{[1
]}

机构：

[1] Tallinn Univ Technol, Informat Syst Grp, Tallinn, Estonia

[2] Tallinn Univ Technol, Software Sci Dept, Tallinn, Estonia

[3] Univ Tartu, Inst Comp Sci, Data Syst Grp, Tartu, Estonia

来源：

2021 THE 3RD INTERNATIONAL CONFERENCE ON BIG DATA ENGINEERING AND TECHNOLOGY, BDET 2021 | 2021年

关键词：

Big data analytics; Association rule mining; Spark; MapReduce; systematic literature review; ALGORITHM; SPARK;

D O I：

10.1145/3474944.3474951

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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).

引用

页码：40 / 49

页数：10

共 43 条

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