This contribution presents our concept for collaborative and guided visual analytics of astrophysical and planetary data developed within the EXPLORE EU project (https://explore-platform.eu). We implement and evaluate new concepts using Visualizer [1], our web-based open-source visual analytics framework. Visualizer is designed for rapid prototyping of research applications, with easy-to-use extensibility and integrability mechanisms built into its core. Adding new visualizations is as simple as implementing a small JavaScript API and uploading the visualization code to the running system. This enables software developers but also researchers with little programming experience to easily integrate custom visualizations. Similarly, new data analysis algorithms can be integrated by wrapping them with an algorithm-agnostic execution REST-API. An API enables users to easily integrate a dashboard designed in Visualizer into any web-based application. In this work, Visualizer is used for development of astrophysical and planetary scientific data applications (SDAs) which are currently being extended to provide collaboration and user guidance methods.

Initial collaboration features enabled users to easily share visual analysis interfaces by exchanging URLs or QR codes. In addition, real-time collaboration methods make it possible for users to collaboratively explore their data or even design visual interfaces together. However, literature research, e.g., [2], as well as requirements from the EXPLORE SDAs revealed that data annotation methods represent another major requirement for collaborative visual analytics. Annotation features enable users to mark and describe any data of interest within visualizations, as well as to share findings and rate each other’s annotations. These features also provide a discussion platform enabling domain experts to collaboratively explore their data, discuss interesting findings, and exchange opinions. For non-experts annotations provide a source of additional knowledge enabling them to better understand complex data sets, relationships, or correlations. For machine learning algorithms the annotations might be used as (additional) training data to improve their performance.

To support the data exploration process, we propose user guidance methods for suggesting and (optionally) automating analytical workflows. Typically, to obtain the desired analytical result the user manually selects and configures various algorithmic and visual data analysis steps and applies them. Given an analytical goal (e.g., outlier detection or correlation analysis), our guidance system supports novice users by recommending analytical workflows that consist of several analytical steps. Relying on provided result previews, the user can select and execute a whole workflow. Alternatively, a more advanced user can reconfigure (parts of) a recommended workflow by manually changing and parametrizing algorithms and visualizations. To offer user guidance, an AI method learns how (experienced) users analyze data and predicts analytical steps for other users. Different approaches have been investigated within our research framework [3] including Markov chains [4] and deep learning approaches. Both approaches learn from user behavior, which is either collected implicitly, e.g., user interaction logging, or explicitly, e.g., user ratings of analytical results.

EXPLORE will deliver six SDAs involving galactic stellar and lunar data, with collaborative visual analytics contributions planned for most of them. For example, we integrated a new 3D volumetric visualization and used it in the G-Tomo SDA (Fig. 1) to represent extinction data cubes. The 3D cube visualization was extended with the interaction functionality supporting creation of 2D slices which, using the Visualizers view coordination framework in the background, are then shown in the contour plot. In the guided analytics example (Fig. 2) the user selects an analytical goal - in this case anomaly detection – and the framework suggests analytical workflows, i.e., sequences of algorithmic and visual methods, that other users employed to achieve that goal. In the S-Phot SDA (Fig. 3) we show the spectral energy distribution of stars using scatterplots and employ a stellar atmosphere model fitting algorithm provided by a consortium partner. Here, annotation is employed to mark some of the outliers (gray data points) as requiring further investigation. In addition, the results of the data analysis can be shared with other users and explored together in real time (Fig. 4).

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004214.

Know-Center is funded within the Austrian COMET Program, under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.

References

[1] Ilija Simic (2018) Visualizer - An Extensible Dashboard for Personalized Visual Data Exploration, Master’s Thesis at Graz University of Technology

[2] M. Elias and A. Bezerianos (2012) Annotating BI Visualization Dashboards: Needs & Challenges, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12)

[3] B. Mutlu, E. Veas and C. Trattner (2016) VizRec: Recommending Personalized Visualizations, ACM Transactions on Interactive Intelligent Systems (TiiS)

[4] Milot Gashi (2018) Goal oriented Visual Recommendations via User Behavior Analysis, Master’s Thesis at Graz University of Technology