An Innovative Framework for Supporting Cognitive-Based Big Data Analytics for Frequent Pattern Mining

The increasing size of modern applications and services produces huge volumes of a wide variety of valuable data of different veracity at a high velocity, which in turn leads to a new challenge to big data analytics. Researchers often use these 5V's (volume, variety, value, veracity, and veloci...

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Vydané v:2018 IEEE International Conference on Cognitive Computing (ICCC) s. 49 - 56
Hlavní autori: Deng, Deyu, Leung, Carson K., Wodi, Bryan H., Yu, Jialiang, Zhang, Hao, Cuzzocrea, Alfredo
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2018
Predmet:
Big Data
cognitive computing
Cognitive systems
Data mining
frequent patterns
Itemsets
knowledge discovery
Sensors
Spark
Uncertainty
vertical mining
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Shrnutí:The increasing size of modern applications and services produces huge volumes of a wide variety of valuable data of different veracity at a high velocity, which in turn leads to a new challenge to big data analytics. Researchers often use these 5V's (volume, variety, value, veracity, and velocity) to describe the features of big data. The interest of discovering patterns from a large collection of data has risen in business for transforming goods into services. Rich sources of big data include complex sensing-centered service systems. Embedded in these big data are useful information and knowledge. In this paper, we present an innovative framework for supporting cognitive-based big data analytics for frequent pattern mining. Evaluation results show the applicability of our framework to support cognitive computing for big data analytics of frequent patterns.
DOI:10.1109/ICCC.2018.00014