Welcome to the VISSECT project page


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.

The three main challenges in the process of segmenting and labeling multivariate time series data.

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.


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..


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.