Extracting knowledge patterns in a data lake for management effectiveness

Abstract

With the correlation collision between different types of data becomes more and more intense, a meaningful and far-reaching data revolution has arrived. Enterprises urgently require a hybrid data platform that can effectively break data silos, and unify data aggregation and sharing. Once the data lake was born, it has been a promising method for enterprises to profoundly improve their Business Intelligence. In this paper, we combine principle component analysis (PCA) with a network-based approach to extract a visual knowledge pattern from data sources in data lake, so as to improve management effectiveness.