A Polyline-based Visualization Technique for Tagged Time-varying Data

Abstract

We have various interesting time-varying data in our daily life, such as weather data (e.g., temperature and air pressure) and stock prices. Such time-varying data is often associated with other information: for example, temperatures can be associated with weather, and stock prices can be associated with social or economic incidents. Meanwhile, we often draw large-scale time-varying data by multiple polylines in one space to compare the time variation of multiple values. We think it should be interesting if such time-varying data is effectively visualized with their associated information. This paper presents a technique for polyline-based visualization and level-of-detail control of tagged time-varying data. Supposing the associated information is attached as tags of the time-varying values, the technique generates clusters of the time-varying values grouped by the tags, and selects representative values for each cluster, as a preprocessing. The technique then draws the representative values as polylines. It also provides a user interface so that users can interactively select interesting representatives, and explore the values which belong to the clusters of the representatives.