Visualization of streaming data: Observing change and context in information visualization techniques

Visualizing data streams poses numerous challenges in the data, image and user space. In the era of big data, we need incremental visualization methods that will allow the analysts to explore data faster and help them make important decisions on time. In this paper, we have reviewed several well-known information visualization methods that are commonly used to visualize static datasets and analyzed their degrees of freedom. By observing which independent visual variables can change in each method, we described how these changes are related to the attribute and structure changes that can occur in the data stream. Most of the changes in the data stream lead to potential loss of temporal and relational context between the new data and the past data. We present potential directions for measuring the amount of change and loss of context by reviewing related work and identify open issues for future work in this domain.