Knowledge Generation in Visual Analytics


Visual Analytics (VA) combines human and machine capabilities in order to generate knowledge from data. Systems are capable of processing large amounts of data while humans reason about their problems by leveraging their knowledge. Humans participate in the VA process by steering the analysis process through interactions with the system. Most current approaches have two major drawbacks: 1) on the one hand, analysts cannot externalize their expert knowledge during the analysis process; 2) on the other hand, they do not understand the processes happening in the system. The project aims at bringing human and machine closer together in order to enhance VA for a more effective and efficient data analysis. This will be achieved by bridging data and analytic provenance with automated and visualization methods. The two types of provenance information will be used to support and automate human knowledge generation processes adaptively according to the users’ needs in their current analysis stage. A major part of this research is to bridge the gap between human and machine learning (ML) in order to make complex model configuration and interaction more accessible and usable. Further, we aim to mitigate human errors during analysis processes caused by cognitive biases. The machine can be used as an unbiased counter party. The unique feature of this research proposal is a holistic perspective onto the entire knowledge generation process in VA with the goal of enhancing the state of the art by developing methods along the entire VA-pipeline. The investigated methods will be applied to several real world datasets, domains, tasks, and users in the analysis areas of flight trajectories, political debates, and subspace clustering in high-dimensional data. The benefits of this research will be evaluated through user studies illustrating the benefits of the novel methods, which enhance the data analysis process to be more accessible, effective, efficient, transparent, and reliable.

  • WG Keim (Data Analysis and Visualization)
    Miller, Matthias; Rauscher, Julius; Keim, Daniel A.; El-Assady, Mennatallah (2022): CorpusVis : Visual Analysis of Digital Sheet Music Collections Computer Graphics Forum. The Eurographics Association. 2022, 41(3), pp. 283-294. ISSN 0167-7055. eISSN 1467-8659. Available under: doi: 10.1111/cgf.14540

CorpusVis : Visual Analysis of Digital Sheet Music Collections


Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.

Origin (projects)

Funding sources
Name Finanzierungstyp Kategorie Project no.
Sachbeihilfe/Normalverfahren third-party funds research funding program 566/17
Further information
Period: 09.03.2017 – 08.03.2020