Visual Analytics Workflows Represented in ATWL

This collection presents a series of published visual analytics workflows reformulated in the Analytic Task and Workflow Language (ATWL). Each example pairs a concise prose summary of the workflow with its full ATWL representation, providing a uniform, machine-readable description of the artifacts produced and the transformations that act on them. The examples are drawn from diverse application domains — including movement, networks, event sequences, topic modelling, spatio-temporal modelling, and machine-learning diagnostics — and together illustrate how a small set of generic intents and artifact categories can capture the structure of very different analytical processes.
Highlighting legend: artifact / transform · workflow / loop / if · entities / feature / … · define-unit / characterise / … · intent / manner / input / … · human / machine / hybrid · "strings" · # comments · → :=

Examples

  1. Cluster-calendar workflow
    Jarke J. van Wijk and Edward R. van Selow. Cluster and calendar based visualization of time series data. In Proceedings of the IEEE Symposium on Information Visualization (InfoVis '99), pages 4–9, Los Alamitos, CA, USA, 1999. IEEE Computer Society. doi: 10.1109/INFVIS.1999.801851
  2. Dynamic Network Exploration workflow
    Stef van den Elzen, Danny Holten, Jorik Blaas, and Jarke J. van Wijk. Reducing snapshots to points: A visual analytics approach to dynamic network exploration. IEEE Transactions on Visualization and Computer Graphics, 22(1):1–10, January 2016. doi: 10.1109/TVCG.2015.2468078
  3. Visual Analysis of Mass Mobility Dynamics (MobilityGraphs)
    Tatiana von Landesberger, Felix Brodkorb, Philipp Roskosch, Natalia Andrienko, Gennady Andrienko, and Andreas Kerren. MobilityGraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Transactions on Visualization and Computer Graphics, 22(1):11–20, 2016. doi: 10.1109/TVCG.2015.2468111
  4. EventFlow workflow
    Megan Monroe, Rongjian Lan, Hanseung Lee, Catherine Plaisant, and Ben Shneiderman. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics, 19:2227–2236, 2013. doi: 10.1109/TVCG.2013.200
  5. EventAction: temporal event sequence recommendation
    F. Du, C. Plaisant, N. Spring and B. Shneiderman, "EventAction: Visual analytics for temporal event sequence recommendation," 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), Baltimore, MD, USA, 2016, pp. 61-70. doi: 10.1109/VAST.2016.7883512
  6. Extracting significant places from trajectories
    Gennady Andrienko, Natalia Andrienko, Christophe Hurter, Salvatore Rinzivillo, and Stefan Wrobel. From movement tracks through events to places: Extracting and characterizing significant places from mobility data. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 161–170, 2011. doi: 10.1109/VAST.2011.6102454
  7. Progressive clustering of trajectories
    Salvatore Rinzivillo, Dino Pedreschi, Mirco Nanni, Fosca Giannotti, Natalia Andrienko, and Gennady Andrienko. Visually-driven analysis of movement data by progressive clustering. Information Visualization, 7:225–239, 2008. doi: 10.1057/PALGRAVE.IVS.9500183
  8. Human-Steered Topic Modelling
    J. Choo, C. Lee, C. K. Reddy and H. Park, "UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization," in IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 1992-2001, Dec. 2013. doi: 10.1109/TVCG.2013.212
  9. Progressive Abstraction Analysis of Multivariate Temporal Data
    Andrienko, N., Andrienko, G. and Shirato, G. (2023), Episodes and Topics in Multivariate Temporal Data. Computer Graphics Forum, 42: e14926. doi: 10.1111/cgf.14926
  10. Partition-based Regression Modelling
    T. Mühlbacher and H. Piringer, "A Partition-Based Framework for Building and Validating Regression Models," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 1962-1971, Dec. 2013. doi: 10.1109/TVCG.2013.125
  11. Spatio-temporal analysis and modelling
    N. Andrienko and G. Andrienko, "A visual analytics framework for spatio-temporal analysis and modelling," Data Mining and Knowledge Discovery, vol. 27, no. 1, 2013, pp. 55-83. doi: 10.1007/s10618-012-0285-7
  12. Feature engineering for behaviour pattern recognition
    N. Andrienko, G. Andrienko, A. Artikis, P. Mantenoglou and S. Rinzivillo, "Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time Series," in IEEE Computer Graphics and Applications, vol. 44, no. 3, pp. 14-29, May-June 2024. doi: 10.1109/MCG.2024.3379851
  13. Exploratory Model Analysis
    Cashman, D., Humayoun, S.R., Heimerl, F., Park, K., Das, S., Thompson, J., Saket, B., Mosca, A., Stasko, J., Endert, A., Gleicher, M. and Chang, R. (2019), A User-based Visual Analytics Workflow for Exploratory Model Analysis. Computer Graphics Forum, 38: 185-199. doi: 10.1111/cgf.13681
  14. Diagnosing binary classifiers
    J. Krause, A. Dasgupta, J. Swartz, Y. Aphinyanaphongs and E. Bertini, "A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations," 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), Phoenix, AZ, USA, 2017, pp. 162-172. doi: 10.1109/VAST.2017.8585720
  15. Interactive Exploration of Trained Ensemble Classifier
    Eirich, J., Münch, M., Jäckle, D., Sedlmair, M., Bonart, J. and Schreck, T. (2022), RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines. Computer Graphics Forum, 41: 302-315. doi: 10.1111/cgf.14452
  16. Exploring Deep Learning Models in TensorFlow
    Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, and Martin Wattenberg, "Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow," IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 1-12, Jan. 2018. doi: 10.1109/TVCG.2017.2744878
  17. What-If Probing of ML Models
    Wexler, James, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda B. Viégas and Jimbo Wilson. "The What-If Tool: Interactive Probing of Machine Learning Models." IEEE Transactions on Visualization and Computer Graphics 26 (2019): 56-65. doi: 10.1109/TVCG.2019.2934619