In the last years, the Visual Analytics (VA) community has made a considerable step towards
Machine Learning (ML). Conceptual models for knowledge generation have been proposed that seek to
combine VA and ML, VA approaches have been presented that make massive use of ML techniques in a
visual-interactive way. Finally, real-world applications (involving domain experts from various
application areas) have been designed, making use of the combined strengths of VA and ML.
In my talk, I will share my experiences on the conflation of ML with VA, by the example of
time-oriented data. Time-oriented data is a data type with the characteristics that every value
depends on a specific point in time. This characteristics allows to store special natural or
human-made phenomena. As such, time-oriented data has special analytical potential for researchers,
engineers, or doctors (to name experts groups of my personal collaboration record), but also has
special challenges that we are confronted with. After several collaborative projects, a PhD thesis,
and a considerable amount of publications that involved time-oriented data, I will reflect on
visual-interactive machine learning for time-oriented data in two parts.
First, I will talk about exploratory search, a concept that combines two complementary analysis
tasks with the goal to create increased analytical benefit in the knowledge generation process. I
will emphasize three aspects that allow the combination of VA and ML: (i) preprocessing, (ii)
clustering and dimensionality reduction, and (iii) seeking relations between data content and
Second, I want to share research results for the segmentation and labeling of time-oriented
data. Segmentation is the principle to divide complex (time-oriented) data into yet more meaningful
units, while labeling refers to the principle to create meaning to data, e.g., to segments. Again,
combinations of VA and ML techniques have proven to be beneficial to achieve useful solutions.
JÃ¼rgen Bernard is Post-doc at the department of Computer Science, TU Darmstadt, Germany. He is
leading the Visual-Interactive Machine Learning research group in the Interactive Graphics Systems
Group (GRIS). He was with Fraunhofer IGD when he received is PhD degree in 2015, entitled â
Exploratory Search in Time-Oriented Primary Dataâ.
In 2016, his work was awarded with the Hugo-Geiger Prize for excellent PhD theses. His research
is in information visualization, visual analytics, machine learning, and user-centered design and
addresses a broad range of application fields with an emphasis on medical health care and
data-driven human-centered health. In 2017, J. Bernard received the Dirk Bartz Prize for Visual
Computing in Medicine from the Eurographics Association (EG).
His data-centered focus is on multivariate data, multimodal data, and time-oriented data. Many
of his visual-interactive solutions combine techniques from cluster analysis, dimensionality
reduction, similarity search, active learning, or other approaches related to information
retrieval, data mining, and machine learning.
His publication list contains over 60 entries, more than half authored by J. Bernard in the
leading position. His publication list includes more than ten journal publications, over 30
conference publications (and conference participations), overall with almost ten award-bringing
University of Konstanz, UniversitÃ¤tsstr. 10, Konstanz
University of Stuttgart, VISUS-Building, Allmandring 19, Vaihingen
Powerwall Room -01.116 (Live Transmission)