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

Publications

  1. NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries
    Yiru Chen, Ryan Li, Austin Mac, Tianbao Xie, Tao Yu, Eugene Wu
    VIS nlviz workshop 2022
  2. How Do Captions Affect Visualization Reading?
    Shelly Cheng, Hazel Zhu, Eugene Wu
    VIS Viscomm 2022
  3. Extending the View Composition Algebra to Hierarchical Data
    Eugene Wu
    arXiV 2022
  4. FaDE: Answering "Why?" Made Fast
    Alexander Yao, Lampros Flokas, Eugene Wu
    In Review 2023
  5. Kitana: A Data-as-a-Service Platform
    Zachary Huang, Pranav Subramaniam, Raul Fernandez, Eugene Wu
    In Review 2023
  6. Calibration: A Simple Trick for Fast Interactive Join Analytics
    Zachary Huang, Eugene Wu
    arXiV 2022
  7. A Grammar for Hypothesis-Driven Visual Analysis
    Ashley Suh, Yilan Jiang, Ab Mosca, Eugene Wu, Remco Chang
    In Review 2023
  8. ConnectorX: Accelerating Data Loading From Databases to Dataframes
    Xiaoying Wang, Weiyuan Wu, Jinze Wu, Yizhou Chen, Nick Zrymiak, Changbo Qu, Lampros Flokas, George Chow, Jiannan Wang, Tianzheng Wang, Eugene Wu, Qingqing Zhou
    In Revision 2023
  9. How I Stopped Worrying About Training Data Bugs and Started Complaining
    Lampros Flokas, Weiuan Wu, Jiannan Wang, Nakul Verma, Eugene Wu
    DEEM Workshop 2022
  10. Interactive Interface Generation in Notebooks
    Jeffrey Tao, Yiru Chen, Eugene Wu
    SIGMOD 2022 demo
  11. PI2: Generating Visual Analysis Interfaces From Queries
    Yiru Chen, Eugene Wu
    SIGMOD 2022
  12. View Composition Algebra for Ad Hoc Comparisons
    Eugene Wu
    TVCG 2022
  13. Dynamic Breakpoints for Y-axis Scales
    Jacob Fisher, Remco Chang, Eugene Wu
    InfoVIS 2021 (short paper)
  14. DIEL: Interactive Visualization Beyond the Here and Now
    Yifan Wu, Remco Chang, Joseph Hellerstein, Arvind Satyanarayan, Eugene Wu
    VIS 2021
  15. Impact of Cognitive Biases on Progressive Visualization
    Marianne Procopio, Ab Mosca, Carlos Scheidegger, Eugene Wu, Remco Chang
    TVCG 2021
  16. Facilitating Exploration with Interaction Snapshots under High Latency
    Yifan Wu, Remco Chang, Joe Hellerstein, Eugene Wu
    InfoVIS (short paper) 2020
  17. Continuous Prefetch for Interactive Data Applications
    Haneen Mohammed, Ziyun Wei, Ravi Netravali, Eugene Wu
    VLDB 2020
  18. Physical Visualization Design
    Lana Ramjit, Zhaoning Kong, Ravi Netravali, Eugene Wu
    SIGMOD (demo) 2020
  19. Monte Carlo Tree Search for Generating Interactive Data Analysis Interfaces
    Yiru Chen, Eugene Wu
    Intelligent Process Automation (IPA) 2020
  20. Acorn: Aggressive Result Caching in Spark SQL
    Lana Ramjit, Matteo Interlandi, Eugene Wu, Ravi Netravali
    SOCC 2019
  21. AlphaClean: Automatic Generation of Data Cleaning Pipelines
    Sanjay Krishnan, Eugene Wu
    ArXiv 2019
  22. Towards Democratizing Relational Data Visualization
    Nan Tang, Eugene Wu, Guoliang Li
    SIGMOD 2019 Tutorial
  23. Precision Interfaces
    Qianrui Zhang, Haoci Zhang, Viraj Rai, Thibault Sellam, Eugene Wu
    SIGMOD 2019
  24. CIDR2: Crazier Innovations in Databases JOIN Reinforcement-learning Research
    Eugene Wu
    CIDR 2019 Abstract
  25. Ten Years of Web Tables
    Michael Cafarella, Alon Halevy, Daisy Zhe Wang, Hongrae Lee, Jayant Madhavan, Cong Yu, Eugene Wu
    PVLDB 2018 Invited Paper,
  26. At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity
    Gabriel Ryan, Abigail Mosca, Remco Chang, Eugene Wu
    InfoVIS 2018 Code
  27. Provenance in Interactive Visualizations
    Fotis Psallidas, Eugene Wu
    HILDA 2018
  28. Precision Interfaces for Different Modalities
    Haoci Zhang, Viraj Rai, Thibault Sellam, Eugene Wu
    SIGMOD (demo) 2018
  29. Demonstration of Smoke: A Deep Breath of Data-Intensive Lineage Applications
    Fotis Psallidas, Eugene Wu
    SIGMOD (demo) 2018
  30. Smoke: Fine-grained Lineage at Interactive Speeds
    Fotis Psallidas, Eugene Wu
    VLDB 2018
  31. Mining Precision Interfaces From Query Logs
    Haoci Zhang, Thibault Sellam, Eugene Wu
    Tech Report 2017
  32. Load-n-Go: Fast Approximate Join Visualizations That Improve Over Time
    Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco Chang
    DSIA 2017
  33. Approximate Entropy as a Measure of Line Chart Complexity
    Gabriel Ryan, Abigail Mosca, Eugene Wu, Remco Chang
    InfoVIS Poster 2017
  34. Towards a Bayesian Model of Data Visualization Cognition
    Yifan Wu, Larry Xu, Remco Chang, Eugene Wu
    DECISIVE 2017
  35. Precision Interfaces
    Haoci Zhang, Thibault Sellam, Eugene Wu
    HILDA 2017
  36. Skipping-oriented Partitioning for Columnar Layouts
    Liwen Sun, Michael J. Franklin, Jiannan Wang, Eugene Wu
    VLDB 2017
  37. 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
  38. A DeVIL-ish Approach to Inconsistency in Interactive Visualizations
    Yifan Wu, Joe Hellerstein, Eugene Wu
    HILDA 2016
  39. PFunk-H: Approximate Query Processing using Perceptual Models
    Daniel Alabi, Eugene Wu
    HILDA 2016
  40. TrendQuery: A System for Interactive Exploration of Trends
    Niranjan Kamat, Eugene Wu, Arnab Nandi
    HILDA 2016
  41. Graphical Perception in Animated Bar Charts
    Eugene Wu, Lilong Jiang, Larry Xu, Arnab Nandi
    Arxiv 2016
  42. Towards Perception-aware Interactive Data Visualization Systems
    Eugene Wu, Arnab Nandi
    DSIA 2015 Slides
  43. The Case for Data Visualization Management Systems
    Eugene Wu, Leilani Battle, Samuel Madden
    VLDB 2014
  44. Vertexica: Your Relational Friend for Graph Analytics!
    Alekh Jindal, Praynaa Rawlani, Eugene Wu, Samuel Madden, Amol Deshpande, Mike Stonebraker
    SIGMOD 2014 demo
  45. Scorpion: Explaining Away Outliers in Aggregate Queries
    Eugene Wu, Samuel Madden
    VLDB 2013 (Best-of) Slides
  46. SubZero: a Fine-Grained Lineage System for Scientific Databases
    Eugene Wu, Samuel Madden, Michael Stonebraker
    ICDE 2013 (Best-of)
  47. A Demonstration of DBWipes: Clean as You Query
    Eugene Wu, Samuel Madden, Michael Stonebraker
    VLDB 2012
  48. Partitioning Techniques for Fine-Grained Indexing
    Eugene Wu, Sam Madden
    ICDE 2011
  49. No Bits Left Behind
    Eugene Wu, Carlo Curino, Sam Madden
    CIDR 2011
  50. 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
  51. Relational Cloud: The Case for a Database Service
    Carlo Curino, Evan Jones, Yang Zhang, Eugene Wu, Sam Madden
    MIT Tech Report 2010
  52. TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets
    Philippe Cudre-Mauroux, Eugene Wu, Sam Madden
    ICDE 2010
  53. Demonstration of the TrajStore System
    Eugene Wu, Philippe Cudre-Mauroux, Sam Madden
    VLDB 2009 demo
  54. The Case for RodentStore: An Adaptive, Declarative Storage System
    Philippe Cudre-Mauroux, Eugene Wu, Sam Madden
    CIDR 2009
  55. WebTables: Exploring the Power of Tables on the Web
    Michael Cafarella, Alon Halevy, Daisy Wang, Eugene Wu, Yang Zhang
    VLDB 2008
  56. Uncovering the Relational Web
    Michael Cafarella, Nodira Khoussainova, Daisy Wang, Eugene Wu, Yang Zhang, Alon Halevy
    WebDB 2008
  57. SASE: Complex Event Processing over Streams (Demo)
    Daniel Gyllstrom, Eugene Wu, Hee-Jin Chae, Yanlei Diao, Patrick Stahlberg, Gordon Anderson
    CIDR 2007
  58. High-performance complex event processing over streams
    Eugene Wu, Yanlei Diao, Shariq Rizvi
    SIGMOD 2006
  59. SASE: Complex Event Processing over Streams
    Daniel Gyllstrom, Eugene Wu, Hee-Jin Chae, Yanlei Diao, Patrick Stahlberg, Gordon Anderson
    CoRR 2006
  60. 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. Bulletin 2006
  61. 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
  62. 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