TimeNotes: A Study on Effective Chart Visualization and Interaction Techniques for Time-Series Data

Abstract

Collecting sensor data results in large temporal data sets which need to be visualized, analyzed, and presented. Onedimensional time-series charts are used, but these present problems when screen resolution is small in comparison to the data. This can result in severe over-plotting, giving rise for the requirement to provide effective rendering and methods to allow interaction with the detailed data. Common solutions can be categorized as multi-scale representations, frequency based, and lens based interaction techniques. In this paper, we comparatively evaluate existing methods, such as Stack Zoom [15] and ChronoLenses [38], giving a graphical overview of each and classifying their ability to explore and interact with data. We propose new visualizations and other extensions to the existing approaches. We undertake and report an empirical study and a field study using these techniques.

Viewing Visual Analytics as Model Building

“…Wang and Mueller [WM17] describe a system for derivation of causal models, where the user interactively refines a model derived by a causal inference algorithm by defining meaningful data subdivisions. In system TimeNotes [WBJ16], parts of long time series can be interactively selected for detailed exploration, compared with others, grouped by similarity, and labelled as instances of specific behaviours.…”

“…Both the new and prior knowledge are related to what the analyst is viewing and doing. The existing visual analytics systems and platforms thus support knowledge externalization by enabling the analyst to annotate and comment on visualizations [WSP*06, EKHW08, SvW08, HVW09, WBJ16]. Ribarsky and Fisher [RF16] stress the importance of enabling the analyst to externalize his knowledge at any time, not only at the end.…”

Chronodes

“…Principally, Aigner et al [1] and Brehmer et al [6] provide a series of typologies and guidelines for visualizing timeline data according to a variety of data types and representational criteria. Meanwhile, approaches to “multifocus” timeline visualization [38, 42] compliment such techniques by enabling users to select multiple segments of time from timeline visualizations, which can then be displayed in a consolidated format and used to find similar patterns [43]. Fundamentally, visualizing dissimilar records side by side proposed in Fouse et al [13] enables users to identify high-level correlations between the distinct data streams.…”

Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics

“…As part of our tool, we incorporated a coordinated time graph linked with the other views. Walker et al [32] explored different timeseries visualizations for effective interactions. We found the best choice to be a brush + zoom interaction and visualization.…”