A new method is presented to get insight into univariate time series data. The problem addressed here is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average

and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle

and generate knowledge templates for visually analyzing multi-field, time-varying 3D air quality data. Specifically, we design a set of multi-level knowledge templates to capture important statistical data properties based on the distribution features of air quality data. We develop a fast template synthesis

surprising outcomes. Designers of Electronic Health Record systems (EHRs) have great potential to apply innovative visual methods to support clinical decision-making and research. This work surveys the state-of-the-art of information visualization systems for exploring and querying EHRs, as described

Abstract — Thestudyofaerosolcompositionforairqualityresearchinvolves the analysis of high-dimensional single particle mass spectrometry data. We describe, apply, and evaluate a novel interactive visual framework for dimensionality reduction ofsuchdata. Our framework is based on non-negative matrix

In previous work, we have proposed a tool for Spatiotemporal Pat-tern Query. It matches individual moving object trajectories against a given movement pattern. For example, it can be used to find the situations of Missed Approach in ATC data (Air Traffic Control sys-tems, used for tracking

be further adapted to apply to other cities, both in New Zealand and overseas. The research involved translation of the results of urban airshed modelling into GIS based exposure maps at a census area unit (CAU) scale over the Auckland region. This data was used to identify and compare air pollution spatial

Abstract: We present a novel set of techniques for visualization of very large data sets encoding flight information obtained from Air Traffic Control. The aims of our visualization are to provide a smooth way to explore the available information and find outlier spatio-temporal patterns

research team members at different locations to query, visualize, and process the cross-referenced data layers in order to generate surface level estimates of initial conditions for use in the air quality models.

. 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