ContiMap: Continuous Heatmap for Large Time Series Data

Vung Pham, Ngan Nguyen, Tommy Dang

External link (DOI)
View presentation:2020-10-26T17:00:00ZGMT-0600Change your timezone on the schedule page
2020-10-26T17:00:00Z
Exemplar figure
Conventional heatmap (left) vs. ContiMap (right) for Alibaba cluster dataset. The x-axis is the time sequence, and the y-axis shows the machines. The color scale ranges from dark green to red for low to high CPU utilization percentages correspondingly, and the white areas depict missing data. ContiMap is faster to render and gives a better overview of the data.
Keywords

Abstract

Limited human cognitive load, limited computing resources, and finite display resolutions are the major obstacles for developing interactive visualization systems in large-scale data analysis. Recent technological innovation has significantly improved computing power, such as faster CPUs and GPUs, as well as display resources, including ultra-high-resolution displays and video walls. However, large and complex data is still ahead in the run as we are generating huge amounts of data daily. Our strategy to bridge these gaps is to present the right amount of information through the use of compelling graphics. This paper proposes an approximation algorithm and a web prototype for representing a high-level abstraction of time series based on heatmap designs. Our approach aims to handle a significant amount of time series data arising from various application domains, such as cybersecurity, sensor network, and gene expression analysis.