KAVA-Time

Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data

KAVA-Time is a basic research project in Visual Analytics and is funded by the Austrian Science Fund FWF.

Analytical reasoning for real world problem solving involves large volumes of uncertain, complex, and often conflicting data that analysts need to make sense of. In this context, time-oriented data is commonplace and plays a special role. Due to the distinct characteristics of time, appropriate methods for exploration and analysis are needed. Visual Analytics provides sophisticated methods that combine interactive visual interfaces with automated analysis methods. Ideally, a Visual Analytics environment would adapt itself to the user’s context and domain specifics of the data to analyze. For example when displaying lab results in electronic patient records this could mean to show expected value ranges for healthy patients depending on their context such as gender or age. For representing stock price data, the time axis would suppress weekends and bank holidays to avoid a distorted representation of value change. In a scenario of exploring energy usage in a building, a representation would be chosen that accounts for daily and weekly cycles by aggregating them and aiding to detect the unknown. Solutions like these might be achieved by creating specialized applications for each domain and analysis problem at hand. However, this would cause lots of effort and make maintenance and reuse difficult. To avoid this, we will design Visual Analytics methods that accommodate to different contexts.

Throughout this project, we will study how we can take advantage of explicit expert knowledge in the Visual Analytics process to make analytical reasoning more effective and efficient. We plan to develop and evaluate knowledge specification methods as well as knowledge-assisted visualization and interaction methods for time-oriented data. This encompasses two main objectives: (1) to capture analysts' domain knowledge and explorative interests, and (2) to take advantage of the explicit knowledge in interaction and visualization methods.

Explicit knowledge in the Visual Analytics process

Current approaches mostly rely on static, externally given knowledge and do not emphasize reuse and sharing of these specifications. In contrast to that, we aim to integrate specification methods directly with interactive Visual Analytics methods to allow intuitive and direct refinement of explicit knowledge by analysts. The visualization methods will make use of explicit knowledge by automatically adapting themselves and using abstractions of the input data. For development and evaluation we plan to adopt data and user tasks from an application scenario in IT security.

Tackling this issue will give rise to more effective environments for gaining insights – the possibility to specify, model, and make use of auxiliary information about data and domain specifics in addition to the raw data, will help to better select, tailor, and adjust appropriate methods for visual representation, interaction, and automated analysis.

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