VISSECT - Visual Segmentation and Labeling of Multivariate Time Series

Submitted by Christian Bors on Thu, 3. Nov 2016 - 14:38

This project's goal is to develop new approaches for visually and interactively analyzing multivariate time series through segmentation and labeling. The ground-breaking novel idea is to combine the algorithm selection, the adequate parametrization, and the visualization and exploration of diverse types of uncertainty about the results.

  • Silvia Miksch
  • Theresia Gschwandtner
  • Markus Bögl
  • Christian Bors

October 2016 - March 2020


The overarching objective of this research project is the combination of the three most relevant steps in the process of segmenting and labeling multivariate time series: algorithm selection, parametrization, and uncertainty analysis. Each of these steps is a research problem on its own, and we will contribute novel solutions to all of them. However, only the combination of the different aspects will comprehensively address the important challenge of visual analytics as identified by Jarke van Wijk: "Which model? Which parametrization? And which feature of the data?" In the following we will describe the underlying research questions for the individual aspects with regard to the envisaged interconnections.

Algorithm selection: Segmentation and labeling algorithms divide multivariate time series into smaller segments and label these segments accordingly. The effect of a particular algorithm on a particular data set is not easily predictable and thus finding the best algorithm in a great diversity of existing algorithms is highly demanding.

Parametrization: The effect of different parametrizations on the segmentation process is not easy to understand, and thus finding the best setting is challenging. Particularly in the case of large parameter spaces (many parameters and large value ranges) one typically has to rely on trial and error to find adequate configurations. So, the main goal of this objective is to develop sophisticated visual analytics techniques for a systematic analysis of the parameter spaces.

Uncertainty: The generated segmentation and labeling results may comprise different kinds of uncertainty at different levels. These stem from the selection of algorithms, parameters, and the calculation of multiple competing results.

In sum, our major objective is to investigate each single aspect (algorithms selection, parametriza- tion, and uncertainty assessment) with regard to the other aspects, to observe the interplay, to combine them, and by doing so, to develop a ground-breaking novel visual analytics approach for analyzing time series data.
The three main challenges in the process of segmenting and labeling multivariate time series data.


The Quality Flow and Provenance Graph views of the Data Quality Provenance Explorer. Christian Bors, Theresia Gschwandtner, Silvia Miksch, "Capturing and Visualizing Provenance From Data Wrangling", IEEE Computer Graphics and Applications, vol. 39, pp. 15, 2019. paper
Iterative refinement of the provenance task abstraction framework. Christian Bors, John Wenskovitch, Michelle Dowling, Simon Attfield, Leilani Battle, Alex Endert, Olga Kulyk, Robert Laramee, "A Provenance Task Abstraction Framework", IEEE Computer Graphics and Applications, vol. 39, pp. 15, 2019. paper
An uncertainty quantification cube for multivariate time series Christian Bors, Jürgen Bernard, Markus Bögl, Theresia Gschwandtner, Jörn Kohlhammer, Silvia Miksch, "Quantifying Uncertainty in Multivariate Time Series Pre-Processing", EuroVis Workshop on Visual Analytics (EuroVA), 2019.
Jürgen Bernard, Marco Hutter, Heiko Reinemuth, Hendrik Pfeifer, Christian Bors, Jörn Kohlhammer, "Visual-Interactive Preprocessing of Multivariate Time Series Data", Computer Graphics Forum, vol. 38, pp. 11, 2019. paper
Jürgen Bernard, Christian Bors, Markus Bögl, Christian Eichner, Theresia Gschwandtner, Silvia Miksch, Heidrun Schumann, Jörn Kohlhammer, "Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series", EuroVis Workshop on Visual Analytics (EuroVA) 2018, pp. 49–53, 2018. paper
Christian Bors, Theresia Gschwandtner, Silvia Miksch, "Visually Exploring Data Provenance and Quality of Open Data", EuroVis 2018 - Posters, pp. 9–11, 2018. paper
Markus Bögl, Christian Bors, Theresia Gschwandtner, Silvia Miksch, "Uncertainty types in segmenting and labeling time series data", , 2018. paper
Markus Bögl, Christian Bors, Theresia Gschwandtner, Silvia Miksch, "Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data", , 2018. paper
Paolo Federico, Markus Wagner, Alexander Rind, Albert Amor-Amorós, Silvia Miksch, Wolfgang Aigner, "The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics", Proceedings of the IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017), 2017. paper Video Teaser
Markus Bögl, Peter Filzmoser, Theresia Gschwandtner, Tim Lammarsch, Roger Leite, Silvia Miksch, Alexander Rind, "Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction", Computer Graphics Forum, vol. 36, pp. 227–238, 2017. paper Usage Scenario
Jürgen Bernard, Eduard Dobermann, Markus Bögl, Martin Röhlig, Anna Vögele, Jörn Kohlhammer, "Visual-Interactive Segmentation of Multivariate Time Series", EuroVis Workshop on Visual Analytics (EuroVA), 2016. paper
Theresia Gschwandtner, Heidrun Schuman, Jürgen Bernard, Thorsten May, Markus Bögl, Silvia Miksch, Jörn Kohlhammer, Martin Röhlig, Bilal Alsallakh, "Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model", Poster Proceedings of the Eurographics Conference on Visualization (EuroVis 2015), pp. 3, 2015.