Melola Discovery

← Melola Discovery

Open access Journal SINTA 2

The MapReduce Model on Cascading Platform for Frequent Itemset Mining

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 12 No. 2 (2018) · pp. 149–160

DOI
10.22146/ijccs.34102
ISSN
2460-7258, 1978-1520
Published
2018-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).

Keywords

Collaboration network of the authors

25 authors · 31 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sigit PriyantaUniversitas Gadjah Mada2
Anifudin AzisUniversitas Gadjah Mada1
Praretno WibowoAffiliation unknown1
Juwita MaharantiAffiliation unknown1
Iqnatius Dimas NugrohoUniversitas Gadjah Mada1
Tri Kuntoro PriyambodoUniversitas Gadjah Mada1
Zaied ShouranUniversitas Gadjah Mada1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Friska Ayu ListyaUniversitas Gadjah Mada1
Wahyono WahyonoUniversitas Gadjah Mada1
Riska Amalia PraptiwiUniversitas Gadjah Mada1
Dayan Ramly RamadhanUniversitas Gadjah Mada1
Hendri Kurniawan PrakosaUniversitas Gadjah Mada1
Anindhita NisitasariUniversitas Gadjah Mada1
Raymond LuisUniversitas Gadjah Mada1
Sarah Rosdiana TambunanUniversitas Gadjah Mada1
Luthfia Nurma HapsariUniversitas Gadjah Mada1
Reka AlamsyahUniversitas Gadjah Mada1
Debby AlitaUniversitas Teknokrat Indonesia1
Muslikhah MuslikhahAffiliation unknown1
Elih SudiapermanaAffiliation unknown1
Moh Edi WibowoUniversitas Gadjah Mada1
Agus SihabudinAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">The MapReduce Model on Cascading Platform for Frequent Itemset Mining</dc:title>
	<dc:creator>Rokhman, Nur</dc:creator>
	<dc:creator>Nursanti, Amelia</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Frequent Itemset Mining; MapReduce; Cascading</dc:subject>
	<dc:description xml:lang="en-US">The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2018-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/34102</dc:identifier>
	<dc:identifier>10.22146/ijccs.34102</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 12, No 2 (2018): July; 149-160</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/34102/21762</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>