PacificVis 2024 VisNotes Honorable Mention Award

10. Mai 2024 / cw

Auf der PacificVis 2024 erhielten Franziska Becker und Tanja Blascheck eine Auszeichnung im Short Paper Track VisNotes.

Auf der 17. IEEE Pacific Visualization Conference (PacificVis 2024) wurden Franziska Becker und Tanja Blascheck für ihr Short Paper “TimeSeriesMaker: Interactive Time series Composition in No Time” mit dem VisNotes Honorable Mention Award ausgezeichnet. Herzlichen Glückwunsch!

Die PacificVis 2024 fand vom 23. Bis 26. April in Tokyo, Japan statt. Ziel der PacificVis ist es, Visualisierungsforschung und -technologien insbesondere im asiatisch-pazifischen Raum zu fördern. Der Visualization Notes (VisNotes) Track ist ein Short Paper Track der Konferenz, der vor allem junge Forscher*innen dazu ermutigen soll, ihre Arbeit vorzustellen und mit den Teilnehmenden zu diskutieren.


F. Becker and T. Blascheck, “TimeSeriesMaker: Interactive Time Series Composition in No Time,” in 2024 IEEE 17th Pacific Visualization Symposium (PacificVis), in 2024 IEEE 17th Pacific Visualization Symposium (PacificVis). IEEE Computer Society, 2024.


TimeSeriesMaker is an open-source application to visually compose time series data in an intuitive and shareable manner. Visualization researchers often use time series data in studies about perceptual or cognitive phenomena and many other contexts. However, finding or generating time series data that fits a given scenario is not always easy. Using a component-based architecture, TimeSeriesMaker allows analysts to compose time series data with complex patterns by combining different components, such as noise, a linear trend or a seasonal pattern. An interactive compositor tree of these components lets analysts explore their combinations using different operators. We support reproducibility and transparency by including functionalities that allow analysts to export and share their configuration, which others can use to reload and modify the same time series. In a qualitative online study with visualization researchers, we found that our approach enables them to create a time series based on an example image or their own requirements. However, system usability could be further improved when interacting with the compositor tree. TimeSeriesMaker can be found here:

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