Mining maximal frequent patterns in transactional databases and dynamic data streams: a Spark-based approach

DOI10.1016/J.INS.2017.11.064zbMath1436.68089DBLPjournals/isci/KarimCBAD18OpenAlexW2774676107WikidataQ55082276 ScholiaQ55082276MaRDI QIDQ781917

Oya Deniz Beyan, Chowdhury Farhan Ahmed, Stefan Decker, Michael Cochez, Mohammad Rezaul Karim

Publication date: 20 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: http://urn.fi/URN:NBN:fi:jyu-201712184760

zbMATH Keywords

data miningbig dataprime number theorytransactional databasesApache Sparkdynamic data streamsmaximal frequent patternsnull transactions

Mathematics Subject Classification ID

Database theory (68P15)

Related Items (1)

An algebraic semigroup method for discovering maximal frequent itemsets

Uses Software

• Unnamed Item
• SparkSW
• Spark
• Apache Spark

Cites Work

• An elementary proof of the prime-number theorem
• Unnamed Item

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