Analysis on Big Data Quarrying Algorithms
Big Data involve large-volume, composite, increasing data sets with numerous, independent sources. With the
fast advancements of networking, data storage, and the data gathering capability, Big Data are now swiftly escalating in all
science and engineering domains, together with physical, biological and biomedical sciences. This paper presents a DICE
theorem that describes the features of the Big Data revolution, and proposes a Big Data processing model, from the
information quarrying viewpoint. This data-driven model involves demand driven aggregation of info sources, quarrying and
analysis, user interest modeling, and security and privacy concerns. We analyze the challenging issues in the data-driven
model and also in the Big Data revolution.