Data is a growing part of our culture, and visualizations of data are key to helping people understand and discuss the issues described by the data. Charts and graphs used to be associated with classrooms and laboratories; today they appear in mainstream coverage of weather, politics, sports, and other popular topics. This has happened in part because of advances in the technology for composing visualizations: it’s much easier today to generate custom charts, thanks to a revolution in toolkits for data visualization driven by the academic research community. But it is still difficult to author interactive visualizations that allow users to manipulate charts. Research shows that interactivity helps people better understand and explore data visualizations. A number of interactive visualizations have appeared in popular online newspapers like the New York Times in recent years. Unfortunately, current interactive visualization toolkits are very technical and difficult to use, even for experts. The research funded by this grant attempts to make it far easier to build interactive visualizations. This should substantially simplify the task of specifying interaction, and broaden the population of users and organizations who can craft rich, interactive visualizations.

The research funded by this grant explores a declarative approach to specifying interactive data visualizations called Logical Interaction, realized in a new language called LIL. As a high level goal, LIL is intended to significantly simplify the specification of interactive visualizations, enabling more widespread use of interactive features in data visualizations. The dynamics of interaction introduce unique technical challenges and opportunities, including debugging and testing of asynchronous interaction handlers, and design tradeoffs between scaling up data and maintaining interface responsiveness. The hypothesis of the research is that Logical Interaction can make these challenges much more tractable, and that LIL can engage visualization designers in widespread, creative development of new interactive visualizations.

The work of the grant starts with exploring the fundamental modeling and language design issues in this domain, to develop techniques for composing and analyzing interaction code, and to deliver a prototype language, runtime, and analysis suite that demonstrates the benefits of our ideas. Results of the work will be embodied in a language runtime for Logical Interaction, which will be freely available as open source. The project will evaluate the effectiveness of Logical Interaction in terms of its interactivity and scale, the range of interactive visualizations it naturally supports, and the ability for users of varying skills to learn and use it. The researchers will also experiment with Logical Interaction in university courses on Big Data and Data Science, and share the curricula publicly along with the software. Research results will be published in the scientific literature.

Principal Investigators

Open Source Software

Galleries and Tutorials


  1. Continuous Prefetch for Interactive Data Applications
    Haneen Mohammed, Ziyun Wei, Ravi Netravali, Eugene Wu
    VLDB 2020
  2. Physical Visualization Design
    Lana Ramjit, Zhaoning Kong, Ravi Netravali, Eugene Wu
    SIGMOD (demo) 2020
  3. Monte Carlo Tree Search for Generating Interactive Data Analysis Interfaces
    Yiru Chen, Eugene Wu
    Intelligent Process Automation (IPA) 2020
  4. Programming with Timespans in Interactive Visualizations
    Yifan Wu, Remco Chang, Eugene Wu, Joe Hellerstein
    ArXiv 2019
  5. DIEL: Transparent Scaling for Interactive Visualization
    Yifan Wu, Remco Chang, Eugene Wu, Joe Hellerstein
    ArXiv 2019
  6. Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations
    Dominik Moritz, Bill Howe, Jeffrey Heer
    CHI 2019
  7. Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web
    Matt Conlen, Alex Kale, Jeffrey Heer
    EuroVis 2019
  8. Towards Democratizing Relational Data Visualization
    Nan Tang, Eugene Wu, Guoliang Li
    SIGMOD 2019 Tutorial
  9. Precision Interfaces
    Qianrui Zhang, Haoci Zhang, Viraj Rai, Thibault Sellam, Eugene Wu
    SIGMOD 2019
  10. At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity
    Gabriel Ryan, Abigail Mosca, Remco Chang, Eugene Wu
    InfoVIS 2018
  11. Making Sense of Asynchrony in Interactive Data Visualizations
    Yifan Wu, Larry Xu, Remco Chang, Joseph M. Hellerstein, Eugene Wu
    Technical Report
  12. Approximate Entropy as a Measure of Line Chart Complexity
    Gabriel Ryan, Abigail Mosca, Eugene Wu, Remco Chang
    InfoVIS Poster 2017
  13. Provenance in Interactive Visualizations
    Fotis Psallidas, Eugene Wu
    HILDA 2018
  14. Precision Interfaces for Different Modalities
    HaoCi Zhang, Viraj Rai, Thibault Sellam, Eugene Wu
    SIGMOD (demo) 2018
  15. Demonstration of Smoke: A Deep Breath of Data-Intensive Lineage Applications
    Fotis Psallidas, Eugene Wu
    SIGMOD (demo) 2018
  16. Smoke: Fine-grained Lineage at Interactive Speeds
    Fotis Psallidas, Eugene Wu
    VLDB 2018 Preprint
  17. Load-n-Go: Fast Approximate Join Visualizations That Improve Over Time
    Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco Chang
    DSIA 2017
  18. Formalizing Visualization Design Knowledge as Constraints; Actionable and Extensible Models in Draco
    Dominik Moritz, Chenglong Wang, Greg L. Nelson, Halden Lin, Adam M. Smith, Bill Howe, Jeffrey Heer
    TVCG 2019 InfoVis 2018 Best Paper!
  19. SetCoLa: High-Level Constraints for Graph Layout
    Jane Hoffswell, Alan Borning, Jeffrey Heer
    EuroVis 2018
  20. Augmenting Code with In Situ Visualizations to Aid Program Understanding
    Jane Hoffswell, Arvind Satyanarayan, Jeffrey Heer
    CHI 2018
  21. Visual Debugging Techniques for Reactive Data Visualization
    Jane Hoffswell and Arvind Satyanarayan and Jeffrey Heer
    EuroVis 16
  22. Vega-Lite: A Grammar of Interactive Graphics
    Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, Jeffrey Heer
    TVCG 2017 InfoVis 2016 Best Paper!
  23. Data Tweening: Incremental Visualization of Data Transforms,
    Meraj Ahmed Khan, Larry Xu, Arnab Nandi, Joseph M. Hellerstein
    VLDB 2017
  24. Precision Interfaces
    Haoci Zhang, Thibault Sellam, Eugene Wu
    HILDA 2017
  25. Combining Design and Performance in a Data Visualization Management System
    Eugene Wu, Fotis Psallidas, Zhengjie Miao, Haoci Zhang,Laura Rettig, Yifan Wu, Thibault Sellam
    CIDR 2017
  26. A DeVIL-ish Approach to Inconsistency in Interactive Visualizations
    Yifan Wu, Joe Hellerstein, Eugene Wu
    Hilda 2016