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. EventThread: visual summarization and stage analysis of event sequence data Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, and Nan Cao, "EventThread: Visual Summarization and Stage Analysis of Event Sequence Data," IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1):56–65, 2018. doi: 10.1109/TVCG.2017.2745320
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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

Further examples (not fully checked, without graphical views)

  1. COQUITO workflow J. Krause, A. Perer, and H. Stavropoulos. Supporting Iterative Cohort Construction with Visual Temporal Queries. In IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 91–100, Jan. 2016. doi: 10.1109/TVCG.2015.2467622
  2. Visual Cohort Comparison for Spatial Single-Cell Omics-Data A. Somarakis, M. E. Ijsselsteijn, S. J. Luk, B. Kenkhuis, N. F. C. C. de Miranda, B. P. F. Lelieveldt, and T. Höllt. Visual Cohort Comparison for Spatial Single-Cell Omics-Data. In IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 733–743, Feb. 2021. doi: 10.1109/TVCG.2020.3030336
  3. Coral workflow P. Adelberger, K. Eckelt, M. J. Bauer, M. Streit, C. Haslinger, and T. Zichner. Coral: A Web-Based Visual Analysis Tool for Creating and Characterizing Cohorts. Bioinformatics, vol. 37, no. 23, pp. 4559–4561, 2021. doi: 10.1093/bioinformatics/btab695
  4. DaedalusData workflow A. Wyss, G. Morgenshtern, A. Hirsch-Hüsler and J. Bernard. DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing — A Design Study. In IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 1, pp. 54–64, Jan. 2025. doi: 10.1109/TVCG.2024.3456329
  5. Knowledge-Incorporated Embedding Exploration workflow J. Li and C.-Q. Zhou. Incorporation of Human Knowledge into Data Embeddings to Improve Pattern Significance and Interpretability. In IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 723–733, Jan. 2023. doi: 10.1109/TVCG.2022.3209382
  6. DPVis workflow B. C. Kwon, V. Anand, K. A. Severson, S. Ghosh, Z. Sun, B. I. Frohnert, M. Lundgren, and K. Ng. DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways. In IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 9, pp. 3685–3700, Sept. 2021. doi: 10.1109/TVCG.2020.2985689
  7. EnsembleLens workflow K. Xu, M. Xia, X. Mu, Y. Wang, and N. Cao. EnsembleLens: Ensemble-Based Visual Exploration of Anomaly Detection Algorithms with Multidimensional Data. In IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 109–119, Jan. 2019. doi: 10.1109/TVCG.2018.2864825
  8. explAIner workflow T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady. explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning. In IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 1064–1074, Jan. 2020. doi: 10.1109/TVCG.2019.2934629
  9. Hybrid Visual Steering workflow K. Matković, D. Gračanin, R. Splechtna, M. Jelović, B. Stehno, H. Hauser and W. Purgathofer. Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles. In IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1803–1812, Dec. 2014. doi: 10.1109/TVCG.2014.2346744
  10. IRVINE workflow J. Eirich, J. Bonart, D. Jäckle, M. Sedlmair, U. Schmid, K. Fischbach, T. Schreck, and J. Bernard. IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines. In IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 11–21, Jan. 2022. doi: 10.1109/TVCG.2021.3114797
  11. IVESA – Visual Analysis of Time-Stamped Event Sequences J. Bernard, C.-M. Barth, E. Cuba, A. Meier, Y. Peiris and B. Shneiderman. IVESA – Visual Analysis of Time-Stamped Event Sequences. In IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 4, pp. 2235–2256, April 2025. doi: 10.1109/TVCG.2024.3382760
  12. Jigsaw workflow J. Stasko, C. Görg, and Z. Liu. Jigsaw: Supporting Investigative Analysis through Interactive Visualization. Information Visualization, vol. 7, no. 2, pp. 118–132, 2008. doi: 10.1057/palgrave.ivs.9500180
  13. Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts K. Eckelt, P. Adelberger, M. J. Bauer, T. Zichner, and M. Streit. Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts. IEEE VIS Workshop on Visualization in Biomedical AI, 2022. doi: 10.1101/2022.08.16.503622
  14. LFPeers: Temporal similarity search and result exploration M. Sachdeva, J. Burmeister, J. Kohlhammer, and J. Bernard. LFPeers: Temporal similarity search and result exploration. In Computers & Graphics, vol. 115, pp. 81–95, Oct. 2023. doi: 10.1016/j.cag.2023.06.009
  15. MotionExplorer workflow J. Bernard, N. Wilhelm, B. Krüger, T. May, T. Schreck and J. Kohlhammer. MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation. In IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2257–2266, Dec. 2013. doi: 10.1109/TVCG.2013.178
  16. Multivariate State Transition Graphs workflow A. J. Pretorius and J. J. van Wijk. Visual Analysis of Multivariate State Transition Graphs. In IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 5, pp. 685–692, Sept./Oct. 2006.
  17. NodeTrix: A Hybrid Visualization of Social Networks N. Henry, J.-D. Fekete, and M. J. McGuffin. NodeTrix: A Hybrid Visualization of Social Networks. In IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1302–1309, Nov.–Dec. 2007. doi: 10.1109/TVCG.2007.70582
  18. ParaGlide: Interactive Parameter Space Partitioning for Computer Simulations S. Bergner, M. Sedlmair, T. Möller, S. N. Abdolyousefi, and A. Saad. ParaGlide: Interactive Parameter Space Partitioning for Computer Simulations. In IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 9, pp. 1499–1512, Sept. 2013. doi: 10.1109/TVCG.2013.61
  19. Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis C. Turkay, E. Kaya, S. Balcisoy, and H. Hauser. Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis. In IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 131–140, Jan. 2017. doi: 10.1109/TVCG.2016.2598470
  20. RelEx: Visualization for Actively Changing Overlay Network Specifications M. Sedlmair, A. Frank, T. Munzner and A. Butz. RelEx: Visualization for Actively Changing Overlay Network Specifications. In IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pp. 2729–2738, Dec. 2012. doi: 10.1109/TVCG.2012.255
  21. SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance D. Sacha, M. Kraus, J. Bernard, M. Behrisch, T. Schreck, Y. Asano, and D. A. Keim. SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance. In IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 120–130, Jan. 2018. doi: 10.1109/TVCG.2017.2744805
  22. SensePath: Understanding the Sensemaking Process Through Analytic Provenance P. H. Nguyen, K. Xu, A. Wheat, B. L. W. Wong, S. Attfield and B. Fields. SensePath: Understanding the Sensemaking Process Through Analytic Provenance. In IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 41–50, Jan. 2016. doi: 10.1109/TVCG.2015.2467611
  23. Guided Visual Exploration of Genomic Stratifications in Cancer (StratomeX) M. Streit, A. Lex, S. Gratzl, C. Partl, D. Schmalstieg, H. Pfister, P. J. Park, and N. Gehlenborg. Guided Visual Exploration of Genomic Stratifications in Cancer. Nature Methods, vol. 11, no. 9, pp. 884–885, Sept. 2014. doi: 10.1038/nmeth.3088
  24. Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration T. Torsney-Weir, A. Saad, T. Möller, B. Weber, H.-C. Hege, J.-M. Verbavatz and S. Bergner. Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration. In IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 1892–1901, Dec. 2011. doi: 10.1109/TVCG.2011.248
  25. VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models F. Cheng, D. Liu, F. Du, Y. Lin, A. Zytek, H. Li, H. Qu and K. Veeramachaneni. VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models. In IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 378–388, Jan. 2022. doi: 10.1109/TVCG.2021.3114836