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

被引:10
|
作者
Deng, Deyu [1 ]
Leung, Carson K. [1 ]
Wodi, Bryan H. [1 ]
Yu, Jialiang [1 ]
Zhang, Hao [1 ]
Cuzzocrea, Alfredo [2 ]
机构
[1] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada
[2] Univ Triste, DIA Dept, Trieste, TS, Italy
来源
2018 IEEE INTERNATIONAL CONFERENCE ON COGNITIVE COMPUTING (ICCC) | 2018年
基金
加拿大自然科学与工程研究理事会;
关键词
Data mining; cognitive computing; service computing; knowledge discovery; frequent patterns; big data; data analytics; vertical mining; Spark;
D O I
10.1109/ICCC.2018.00014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:49 / 56
页数:8
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