Data-Driven Decision Support
DDDM helps management make better-informed decisions, but when the data is streaming in from a number of sources, it can complicate the process as management attempts to match and keep up with the velocity of the incoming data. Read this article to explore the challenges of making decisions with streaming data and the adaptation needed in the decision-making framework to continue making informed decisions.
Summary
In this position paper, we propose a framework for adaptive 
M under concept drift in high-volume streaming data environment, elaborate the challenges and opportunities presented by big streaming data, introduce the three steps of learning under concept drift, and discuss future research directions for adaptive decision support.
This paper highlights the issue of real-time M and provides some fundamental knowledge and methodologies for researchers and practitioners in decision support system area. We hope it could provide a good guideline on how to apply concept drift handling methodologies to help M techniques in big streaming data.