Data Visualization
Management System

Rethinking data-driven interactive visualization

Most visualizations today are produced by retrieving data from a database and using a specialized visualization tool to render it. This decoupled approach results in significant duplication of functionality, such as aggregation and filters, and misses tremendous opportunities for cross-layer optimizations–leading to slow, inflexible, difficult-to-scale systems. Furthermore, existing callback-based interactive visualization programming is difficult to write, and impossible to manage and debug.

The Data Visualization Management System, or DVMS, is based on a declarative visualization language that fully compiles the end-to-end visualization pipeline into a set of declarative, relational algebra-like queries. The same way relational algebra defined an optimization point that ushered in todays data-driven world, we are studying the same for interactive data visualizations and interfaces. The DVMS logically manages the entire visualizations process, from data processing to the pixels presented to the user, within a single data model. This allows DVMS to be both expressive via the visualization language, performant by leveraging traditional and visualization-specific optimizations to scale interactive visualizations to massive datasets, and maintainable by enabling more powerful analysis tools.

Mappings of Interactions

Mappings of Interactions is a declarative approach to interaction specification realized through a language called DDI. DDI is intended to radically simplify the specification of interactive visualizations, enabling much more widespread use of interactive features. The dynamics of interaction introduce unique technical challenges and opportu- nities, including debugging and testing of asynchronous interaction handlers, and design tradeoffs between scaling up data and maintaining interface responsiveness. We hypothesize that the Mappings of Interactions can make these classes of challenges much more tractable, and that DDI can engage visualization designers in widespread, creative development of new interactive visualizations.

See project website

Perceptually Accurate Interactive Vis

An often overlooked element of the interactive data visualization stack is the human in the loop. While computational and data processing capabilities have increased over time, human limits have remained constant. In this light, we describe extensions to client-server database-driven visualization systems that are both customized to interactive workloads, and support perceptual models that approximate the human’s ability to decode visually encoded information. We recognize and accommodate human perceptual limitations through the use of perceptual functions, or PFunk as a way to minimize computation, network and rendering costs, and support high frame-rate interactions.

Based on these models, we seek to answer a critical question: how can these models inform approximation decisions that improve end-to-end visualization performance?

See project website


  1. AlphaClean: Automatic Generation of Data Cleaning Pipelines
    Sanjay Krishnan, Eugene Wu
  2. Towards Democratizing Relational Data Visualization
    Nan Tang, Eugene Wu, Guoliang Li
    SIGMOD 2019 Tutorial
  3. Precision Interfaces
    Qianrui Zhang, Haoci Zhang, Viraj Rai, Thibault Sellam, Eugene Wu
    SIGMOD 2019
  4. CIDR2: Crazier Innovations in Databases JOIN Reinforcement-learning Research
    Eugene Wu
    CIDR 2019 Abstract
  5. Ten Years of Web Tables
    Michael Cafarella, Alon Halevy, Daisy Zhe Wang, Hongrae Lee, Jayant Madhavan, Cong Yu, Eugene Wu
    PVLDB 2018 Invited Paper,
  6. At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity
    Gabriel Ryan, Abigail Mosca, Remco Chang, Eugene Wu
    InfoVIS 2018
  7. Provenance in Interactive Visualizations
    Fotis Psallidas, Eugene Wu
    HILDA 2018
  8. Precision Interfaces for Different Modalities
    Haoci Zhang, Viraj Rai, Thibault Sellam, Eugene Wu
    SIGMOD (demo) 2018
  9. Demonstration of Smoke: A Deep Breath of Data-Intensive Lineage Applications
    Fotis Psallidas, Eugene Wu
    SIGMOD (demo) 2018
  10. A "Probabilistic" Model of Research
    Eugene Wu
    Blog Post 2018
  11. Smoke: Fine-grained Lineage at Interactive Speeds
    Fotis Psallidas, Eugene Wu
    VLDB 2018
  12. Mining Precision Interfaces From Query Logs
    Haoci Zhang, Thibault Sellam, Eugene Wu
    Tech Report 2017
  13. Load-n-Go: Fast Approximate Join Visualizations That Improve Over Time
    Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco Chang
    DSIA 2017
  14. Approximate Entropy as a Measure of Line Chart Complexity
    Gabriel Ryan, Abigail Mosca, Eugene Wu, Remco Chang
    InfoVIS Poster 2017
  15. Towards a Bayesian Model of Data Visualization Cognition
    Yifan Wu, Larry Xu, Remco Chang, Eugene Wu
    DECISIVE 2017
  16. Precision Interfaces
    Haoci Zhang, Thibault Sellam, Eugene Wu
    HILDA 2017
  17. Skipping-oriented Partitioning for Columnar Layouts
    Liwen Sun, Michael J. Franklin, Jiannan Wang, Eugene Wu
    VLDB 2017
  18. 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
  19. A DeVIL-ish Approach to Inconsistency in Interactive Visualizations
    Yifan Wu, Joe Hellerstein, Eugene Wu
    Hilda 2016
  20. PFunk-H: Approximate Query Processing using Perceptual Models
    Daniel Alabi, Eugene Wu
    Hilda 2016
  21. TrendQuery: A System for Interactive Exploration of Trends
    Niranjan Kamat, Eugene Wu, Arnab Nandi
    Hilda 2016
  22. Graphical Perception in Animated Bar Charts
    Eugene Wu, Lilong Jiang, Larry Xu, Arnab Nandi
    Arxiv 2016
  23. Towards Perception-aware Interactive Data Visualization Systems
    Eugene Wu, Arnab Nandi
    DSIA 2015 Slides
  24. The Case for Data Visualization Management Systems
    Eugene Wu, Leilani Battle, Samuel Madden
    VLDB 2014
  25. Scorpion: Explaining Away Outliers in Aggregate Queries
    Eugene Wu, Samuel Madden
    VLDB 2013 (Best-of) Slides
  26. SubZero: a Fine-Grained Lineage System for Scientific Databases
    Eugene Wu, Samuel Madden, Michael Stonebraker
    ICDE 2013 (Best-of)
  27. A Demonstration of DBWipes: Clean as You Query
    Eugene Wu, Samuel Madden, Michael Stonebraker
    VLDB 2012
  28. Partitioning Techniques for Fine-Grained Indexing
    Eugene Wu, Sam Madden
    ICDE 2011
  29. No Bits Left Behind
    Eugene Wu, Carlo Curino, Sam Madden
    CIDR 2011
  30. Relational Cloud: A Database-as-a-Service for the Cloud
    Carlo Curino, Evan Jones, Raluca Popa, Nirmesh Malviya, Eugene Wu, Sam Madden, Hari Balakrishnan, Nickolai Zeldovich
    CIDR 2011
  31. Relational Cloud: The Case for a Database Service
    Carlo Curino, Evan Jones, Yang Zhang, Eugene Wu, Sam Madden
    MIT Tech Report 2010
  32. TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets
    Philippe Cudre-Mauroux, Eugene Wu, Sam Madden
    ICDE 2010
  33. Demonstration of the TrajStore System
    Eugene Wu, Philippe Cudre-Mauroux, Sam Madden
    VLDB 2009
  34. The Case for RodentStore: An Adaptive, Declarative Storage System
    Philippe Cudre-Mauroux, Eugene Wu, Sam Madden
    CIDR 2009
  35. WebTables: Exploring the Power of Tables on the Web
    Michael Cafarella, Alon Halevy, Daisy Wang, Eugene Wu, Yang Zhang
    VLDB 2008
  36. Uncovering the Relational Web
    Michael Cafarella, Nodira Khoussainova, Daisy Wang, Eugene Wu, Yang Zhang, Alon Halevy
    WebDB 2008
  37. SASE: Complex Event Processing over Streams (Demo)
    Daniel Gyllstrom, Eugene Wu, Hee-Jin Chae, Yanlei Diao, Patrick Stahlberg, Gordon Anderson
    CIDR 2007
  38. High-performance complex event processing over streams
    Eugene Wu, Yanlei Diao, Shariq Rizvi
    SIGMOD 2006
  39. SASE: Complex Event Processing over Streams
    Daniel Gyllstrom, Eugene Wu, Hee-Jin Chae, Yanlei Diao, Patrick Stahlberg, Gordon Anderson
    CoRR 2006
  40. Probabilistic Data Management for Pervasive Computing: The Data Furnace Project
    Minos N. Garofalakis, Kurt P. Brown, Michael J. Franklin, Joseph M. Hellerstein, Daisy Zhe Wang, Eirinaios Michelakis, Liviu Tancau, Eugene Wu, Shawn R. Jeffery, Ryan Aipperspach
    IEEE Data Eng. Bull. 2006
  41. Design Considerations for High Fan-In Systems: The HiFi Approach
    Michael J. Franklin, Shawn R. Jeffery, Sailesh Krishnamurthy, Frederick Reiss, Shariq Rizvi, Eugene Wu, Owen Cooper, Anil Edakkunni, Wei Hong
    CIDR 2005
  42. HiFi: A Unified Architecture for High Fan-in Systems
    Owen Cooper, Anil Edakkunni, Michael J. Franklin, Wei Hong, Shawn R. Jeffery, Sailesh Krishnamurthy, Frederick Reiss, Shariq Rizvi, Eugene Wu
    VLDB 2004 Demo