Data mining with big data

TLDR

Big Data refers to large‑volume, complex, rapidly expanding datasets from multiple autonomous sources across science and engineering, driven by advances in networking, storage, and collection. The paper introduces a HACE theorem that characterizes the Big Data revolution and proposes a data‑mining processing model, while analyzing the challenges of this model and the revolution. The proposed model aggregates information sources on demand, performs mining and analysis, models user interests, and incorporates security and privacy safeguards.

Abstract

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.