An Innovative Framework for Supporting Cognitive-Based Big Data Analytics for Frequent Pattern Mining
Deyu Deng, Carson Kai-Sang Leung, Bryan H. Wodi, Jialiang Yu, Hao Zhang, Alfredo Cuzzocrea · 2018
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.