Visualizing time-series on spirals

By Marc Weber, Marc Alexa, Wolfgang Müller. IEEE Symposium on Information Visualization, 2001. INFOVIS 2001., 2001

396 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c518352c6711ca3df41371eee10b80e14fc8863d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Yingcai Wu
3. Ben Shneiderman
4. Tak-Chung Fu
5. Catherine Plaisant
6. Panpan Xu
7. Huamin Qu
8. Enxun Wei

In Signal Processing

1. Shixia Liu
2. Yingcai Wu
3. Tak-Chung Fu
4. Enxun Wei
5. Panpan Xu
6. H. Schumann
7. Silvia Miksch
8. Wolfgang Aigner

Citations in the corpus

• Under Consideration for Publication in Knowledge and Information Systems Improving Svm Classification on Imbalanced Time Series Data Sets with Ghost Points (neutral): “There have been some advances in the visualization of time series (for example, [29]), but the line graph is still the most prevalent.”
• A review on time series data mining (2011, methodology): “…series include: (1) cluster and calendarbased visualization tool (van Wijk and van Selow, 1999), which obtains chunks of data with a given interval and then clusters them accordingly and (2) spiral visualization tool (Weber et al., 2001), which maps the periodic section of time series into a ring.”
• Visual Analysis of Topic Competition on Social Media (2013, background): “Time is often represented by the horizontal axis [21, 29], or spirals to highlight the periodical patterns [44].”
• Visual Methods for Analyzing Time-Oriented Data (2008, methodology): “The Spiral Graph [9] (see also [10] and [11]) is a visualization technique that focuses on cyclic characteristics of time-oriented data by using a spirally shaped time axis (see Figs.”
• Detecting concept drift in data streams using model explanation (2018, background): “g in spiral graphs (Pack, 2012; Weber et al., 2001) or even more exotic visualizations (Moere, 2004).”
• LifeFlow: visualizing an overview of event sequences (2011, background): “spired by the cyclic nature of how we organize time and used to reveal periodic patterns in long time series [8, 15, 36].”
• Aligning temporal data by sentinel events: discovering patterns in electronic health records (2008, methodology): “However, these alignments are non-interactive [12,19], non-data-driven [4, 22 ], or require manual specification of objects or time points to be aligned [7].”
• Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods (2018, background): “It is effective in visualizing large datasets and allows for an easy detection of cycles [37].”