KnoVA – Knowledge–Assisted Visual Analytics

Aim

The overall vision that drives this research proposal can be captured in the following working hypothesis/aim:

"The adoption of knowledge-assisted Visual Analytics methods, in which the different processes involved can rely on explicit prior knowledge, results in an improved data analysis, with a reduction of the cognitive load for users, and an increase in number and quality of their insights."

Duration
-
Funding

Austrian Science Fund (FWF), grant P31419-N31.

Contact
Status
ongoing

Nowadays increasing amounts of data about complex phenomena are collected, stored, and made available for analysis.  Visual Analytics (VA), “the science of analytical reasoning facilitated by interactive visual interfaces”, is a multidisciplinary approach to make sense of this data, combining the enormous processing power of computers with the outstanding perceptual and cognitive capabilities of humans. Users of VA systems need to rely on prior knowledge to gain insights from data, formulate and test hypotheses, interpret results, and discover new knowledge. Users’ tacit knowledge is taken into account for designing visualization methods, but the systematic utilization of explicit knowledge is largely unexplored. Existing knowledge-assisted approaches put a stronger emphasis on operational (how to interact) than domain (how to interpret) knowledge; they focus mainly on visualization and disregard other VA aspects.  In the proposed project, we tackle the following research question: how can the VA process benefit from explicit knowledge in terms of enhanced interactive visualization and automated data analysis capabilities?  Our hypothesis is that a knowledge-based VA approach, utilizing explicit knowledge across all VA stages, improves the analysis by increasing number and quality of the insights found by users while reducing their cognitive load.

The main scientific innovation of our approach lies on the enhancement of the VA process by making use of available knowledge repositories, also collected for different purposes than data analysis tasks. These knowledge bases, integrated within VA systems, will be exploited to assist not only visual mapping and interactive exploration, but also automated analytical methods. In particular, we will enhance VA methods by adapting the visualization and data mining processes in order to leverage available prior explicit knowledge and introducing complementary knowledge-assisted processes, like simulation and guidance.

We will apply human-centered design methods, in particular the well-established nested model for visualization design and validation. Despite the conceptual generality of our proposed approach, we will tailor it in specific application domains. In the medical domain, for example, we will use the knowledge formalized in computer-interpretable clinical practice guidelines to improve the analysis of electronic health records. A continuous involvement of domain experts will enable iterative refinements of developed artifacts (conceptual models, mock-ups, functional prototypes) and validation of results.

Publications

Christina Stoiber, Davide Ceneda, Markus Wagner, Victor Schetinger, Theresia Gschwandtner, Marc Streit, Silvia Miksch, Wolfgang Aigner, "Perspectives of Visualization Onboarding and Guidance in VA", Visual Informatics, vol. 6, pp. 15, 2022. paper
Natalia Andrienko, Gennady Andrienko, Silvia Miksch, Heidrun Schumann, Stefan Wrobel, "A Theoretical Model for Pattern Discovery in Visual Analytics", Visual Informatics, vol. 5, pp. 20, 2021. paper
Davide Ceneda, Alessio Arleo, Theresia Gschwandtner, Silvia Miksch, "Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual Analytics", IEEE Transactions on Visualization and Computer Graphics, pp. 12, 2021. paper
Silvia Miksch, "Visual Analytics Meets Process Mining: Challenges and Opportunities", 2021 3rd International Conference on Process Mining (ICPM), 2021.
Silvia Miksch, Heike Leite, Min Chen, "Knowledge-Assisted Visualization and Guidance", Foundations of Data Visualization, pp. 61--85, 2020. paper
Roger Leite, Victor Schetinger, Davide Ceneda, Bernardo Henz, Silvia Miksch, "COVIs: Supporting Temporal Visual Analysis of Covid-19 Events Usable in Data-Driven Journalism", IEEE VIS 2020, short paper, 2020.
Davide Ceneda, "Guidance-Enriched Visual Analytics", Institute of Visual Computing and Human-Centered Technology, vol. PhD, Dr.-techn., pp. 206, 2020. paper
Davide Ceneda, Theresia Gschwandtner, Silvia Miksch, "A review of guidance approaches in visual data analysis: A multifocal perspective", Computer Graphics Forum, vol. 38, pp. 861-879, 2019. paper
Sara Fabrikant, Silvia Miksch, Alexander Wolff, "Visual Analytics for Sets over Time and Space (Dagstuhl Seminar 19192)", , vol. 9, pp. 31-57, 2019. paper