Paper Info
Title
Exploring Large Scale Time-Series Data Using Nested Timelines
Abstract
When data analysts study time-series data, an important task is to discover how data patterns change over time. If the dataset is very large, this task becomes challenging. Researchers have developed many visualization techniques to help address this problem. However, little work has been done regarding the changes of multivariate patterns, such as linear trends and clusters, on time-series data. In this paper, we describe a set of history views to fill this gap. This technique works under two modes: merge and non-merge. For the merge mode, merge algorithms were applied to selected time windows to generate a change-based hierarchy. Contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. In the non-merge mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid-based views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields and distance maps were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time.
Year
DOI
Venue
2013
10.1117/12.2003897
VISUALIZATION AND DATA ANALYSIS 2013
Keywords
DocType
Volume
Time-series data, multivariate data, visual analysis, timeline
Conference
8654
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
21
3
Name
Order
Citations
PageRank
Zaixian Xie1444.05
Matthew O. Ward21757189.48
Elke A. Rundensteiner34076700.65