A snail shell process model for knowledge discovery via data analytics

摘要

The rapid growth of big data environment imposes new challenges that traditional knowledge discovery and data mining process (KDDM) models are not adequately suited to address. We propose a snail shell process model for knowledge discovery via data analytics (KDDA) to address these challenges. We evaluate the utility of the KDDA process model using real-world analytic case studies at a global multi-media company. By comparing against traditional KDDM models, we demonstrate the need and relevance of the snail shell model, particularly in addressing faster turnaround and frequent model updates that characterize knowledge discovery in the big data environment.