Distributed Scalable Association Rule Mining over Covid-19 Data

Mahtab Shahin; Wissem Inoubli; Syed Attique Shah; Sadok Ben Yahia; Dirk Draheim

doi:10.1007/978-3-030-91387-8_3

Distributed Scalable Association Rule Mining over Covid-19 Data

Mahtab Shahin^*
, Wissem Inoubli
, Syed Attique Shah
, Sadok Ben Yahia
, Dirk Draheim

^*Kontaktforfatter

Tallinn University of Technology
University of Tartu
Tallinn University of Technology

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

Abstract

The worldwide Covid-19 widespread in 2020 has turned into a phenomenon that has shaken human life significantly. It is widely recognized that taking faster measurements is crucial for monitoring and preventing the further spread of COVID-19. The advent of distributive computing frameworks provides one efficient solution for the issue. One method uses non-clinical techniques, such as data mining tools and other artificial intelligence technologies. Spark is a widely used framework and accepted by the big data community. This research used a cross-country Covid-19 dataset to assess the performance of the Apriori and FP-growth through different components of Spark (different numbers of cores and transactions). This involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. This research aims to understand the difference between FP-growth and Apriori and find the ideal parameters of Spark that can improve the performance by adding nodes.

Originalsprog	Engelsk
Titel	Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings
Redaktører	Tran Khanh Dang, Josef Küng, Tai M. Chung, Makoto Takizawa
Forlag	Springer Science+Business Media
Publikationsdato	2021
Sider	39-52
ISBN (Trykt)	9783030913861
DOI	https://doi.org/10.1007/978-3-030-91387-8_3
Status	Udgivet - 2021
Udgivet eksternt	Ja
Begivenhed	8th International Conference on Future Data and Security Engineering , FDSE 2021 - Virtual, Online Varighed: 24. nov. 2021 → 26. nov. 2021

Konference

Konference	8th International Conference on Future Data and Security Engineering , FDSE 2021
By	Virtual, Online
Periode	24/11/2021 → 26/11/2021

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	13076 LNCS
ISSN	0302-9743

Bibliografisk note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Dokumenter og links

10.1007/978-3-030-91387-8_3

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

Shahin, M., Inoubli, W., Shah, S. A., Yahia, S. B., & Draheim, D. (2021). Distributed Scalable Association Rule Mining over Covid-19 Data. I T. K. Dang, J. Küng, T. M. Chung, & M. Takizawa (red.), Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings (s. 39-52). Springer Science+Business Media. https://doi.org/10.1007/978-3-030-91387-8_3

Shahin, Mahtab ; Inoubli, Wissem ; Shah, Syed Attique et al. / Distributed Scalable Association Rule Mining over Covid-19 Data. Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings. red. / Tran Khanh Dang ; Josef Küng ; Tai M. Chung ; Makoto Takizawa. Springer Science+Business Media, 2021. s. 39-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13076 LNCS).

@inproceedings{b7f451ed75b8458c8e25b0c03370017a,

title = "Distributed Scalable Association Rule Mining over Covid-19 Data",

abstract = "The worldwide Covid-19 widespread in 2020 has turned into a phenomenon that has shaken human life significantly. It is widely recognized that taking faster measurements is crucial for monitoring and preventing the further spread of COVID-19. The advent of distributive computing frameworks provides one efficient solution for the issue. One method uses non-clinical techniques, such as data mining tools and other artificial intelligence technologies. Spark is a widely used framework and accepted by the big data community. This research used a cross-country Covid-19 dataset to assess the performance of the Apriori and FP-growth through different components of Spark (different numbers of cores and transactions). This involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. This research aims to understand the difference between FP-growth and Apriori and find the ideal parameters of Spark that can improve the performance by adding nodes.",

keywords = "Apriori, Association rule mining, Big data, FP-growth, Machine learning, Spark",

author = "Mahtab Shahin and Wissem Inoubli and Shah, \{Syed Attique\} and Yahia, \{Sadok Ben\} and Dirk Draheim",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 8th International Conference on Future Data and Security Engineering , FDSE 2021 ; Conference date: 24-11-2021 Through 26-11-2021",

year = "2021",

doi = "10.1007/978-3-030-91387-8\_3",

language = "English",

isbn = "9783030913861",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science+Business Media",

pages = "39--52",

editor = "Dang, \{Tran Khanh\} and Josef K{\"u}ng and Chung, \{Tai M.\} and Makoto Takizawa",

booktitle = "Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings",

address = "United States",

}

Shahin, M, Inoubli, W, Shah, SA, Yahia, SB & Draheim, D 2021, Distributed Scalable Association Rule Mining over Covid-19 Data. i TK Dang, J Küng, TM Chung & M Takizawa (red), Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings. Springer Science+Business Media, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 13076 LNCS, s. 39-52, 8th International Conference on Future Data and Security Engineering , FDSE 2021, Virtual, Online, 24/11/2021. https://doi.org/10.1007/978-3-030-91387-8_3

Distributed Scalable Association Rule Mining over Covid-19 Data. / Shahin, Mahtab; Inoubli, Wissem; Shah, Syed Attique et al.
Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings. red. / Tran Khanh Dang; Josef Küng; Tai M. Chung; Makoto Takizawa. Springer Science+Business Media, 2021. s. 39-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13076 LNCS).

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

TY - GEN

T1 - Distributed Scalable Association Rule Mining over Covid-19 Data

AU - Shahin, Mahtab

AU - Inoubli, Wissem

AU - Shah, Syed Attique

AU - Yahia, Sadok Ben

AU - Draheim, Dirk

PY - 2021

Y1 - 2021

N2 - The worldwide Covid-19 widespread in 2020 has turned into a phenomenon that has shaken human life significantly. It is widely recognized that taking faster measurements is crucial for monitoring and preventing the further spread of COVID-19. The advent of distributive computing frameworks provides one efficient solution for the issue. One method uses non-clinical techniques, such as data mining tools and other artificial intelligence technologies. Spark is a widely used framework and accepted by the big data community. This research used a cross-country Covid-19 dataset to assess the performance of the Apriori and FP-growth through different components of Spark (different numbers of cores and transactions). This involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. This research aims to understand the difference between FP-growth and Apriori and find the ideal parameters of Spark that can improve the performance by adding nodes.

AB - The worldwide Covid-19 widespread in 2020 has turned into a phenomenon that has shaken human life significantly. It is widely recognized that taking faster measurements is crucial for monitoring and preventing the further spread of COVID-19. The advent of distributive computing frameworks provides one efficient solution for the issue. One method uses non-clinical techniques, such as data mining tools and other artificial intelligence technologies. Spark is a widely used framework and accepted by the big data community. This research used a cross-country Covid-19 dataset to assess the performance of the Apriori and FP-growth through different components of Spark (different numbers of cores and transactions). This involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. This research aims to understand the difference between FP-growth and Apriori and find the ideal parameters of Spark that can improve the performance by adding nodes.

KW - Apriori

KW - Association rule mining

KW - Big data

KW - FP-growth

KW - Machine learning

KW - Spark

U2 - 10.1007/978-3-030-91387-8_3

DO - 10.1007/978-3-030-91387-8_3

M3 - Article in proceedings

AN - SCOPUS:85121143505

SN - 9783030913861

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 39

EP - 52

BT - Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings

A2 - Dang, Tran Khanh

A2 - Küng, Josef

A2 - Chung, Tai M.

A2 - Takizawa, Makoto

PB - Springer Science+Business Media

T2 - 8th International Conference on Future Data and Security Engineering , FDSE 2021

Y2 - 24 November 2021 through 26 November 2021

ER -

Shahin M, Inoubli W, Shah SA, Yahia SB, Draheim D. Distributed Scalable Association Rule Mining over Covid-19 Data. I Dang TK, Küng J, Chung TM, Takizawa M, red., Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings. Springer Science+Business Media. 2021. s. 39-52. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13076 LNCS). doi: 10.1007/978-3-030-91387-8_3

Distributed Scalable Association Rule Mining over Covid-19 Data

Abstract

Konference

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater