PerceptVis
Perceptually Aware Interactive
Visualization Systems
Interactive visualizations are a powerful way to explore and draw
insights from data. As the data available to practitioners continues
to grow in complexity and size, existing systems find it harder and
harder to maintain a highly interactive experience. The goal of
this project is to develop an interactive visual data exploration
system designed and optimized to take human perception into account.
The aim of this research is to model human perception as perceptual
functions. These functions help the system avoid unnecessarily
computing visualization results that are more accurate than what
can be perceived by the end user. By developing and using these
functions, the system can provide highly accurate yet interactive
visualizations for large datasets in domains such as business
intelligence, data-driven sciences, and healthcare analytics.
A commonly overlooked element of interactive visualization systems
is the human in the loop. Although data sizes and computational
capabilities have dramatically increased over time, human perceptual
limits have remained relatively constant. Although previous works
have used perceptual insights to justify approximation
algorithms, our goal is to build a multi-layered data analysis
system that unifies interactive visualization clients with backend
data management systems, and explicitly takes human perceptual
models into account. These models can be used to develop
perceptually-aware optimizations such as
- Automatically approximate data transformations that are perceptually indistinguishable
- Model queries generated by an interaction (e.g., dragging a scrollbar to the right) as a single session and optimize across the entire
set of queries, and
- Apply interaction-oriented caching and rewrite
strategies to minimize latency.
Ultimately, these techniques can
ensure high frame-rate interactions for data exploration without
negatively impacting the insights that users draw from their
visualizations.
Award Information
NSF Award: III: Small: Collaborative Research: Towards Interactive Data Visualization Management Systems
REU Supplement: Development of Graphical Perception as a Service
September 1, 2015 — August 31, 2018 (Estimated)
This material is based upon work supported by the National Science Foundation under Collaborative Grant No. (1527779 / 1527765).
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Members
- PI: Arnab Nandi, The Ohio State University
- PI: Eugene Wu, Columbia University
- Graduate Student: Meraj Khan, The Ohio State University
- Masters Student: Daniel Alabi, Columbia University (now Ph.D candidate at Harvard)
- Undergraduate Students: Larry Xu, UC Berkeley; Haoci Zhang, Tsinghua University; Hamed Nilforoshan, Columbia University;
James Sands, Columbia University;
Rahul Khanna, Columbia University
Related Publications
- MA Khan, A Nandi: Flux capacitors for JavaScript deloreans: approximate caching for physics-based data interactionIUI 2019
- Nan Tang, Eugene Wu, Guoliang Li: Towards Democratizing Relational Data Visualization SIGMOD 2019 Tutorial
- Qianrui Zhang, Haoci Zhang, Viraj Rai, Thibault Sellam, Eugene Wu: Precision Interfaces SIGMOD 2019
- Marianne Procopio, Carlos Scheidegger, Eugene Wu and Remco Chang: Selective Wander Join: Fast Progressive Visualizations for Data Joins. INFORMATICS 2019
- Gabriel Ryan, Abigail Mosca, Remco Chang, Eugene Wu At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity InfoVIS 2018
- Lilong Jiang, Protiva Rahman, Arnab Nandi: Evaluating Interactive Data Systems: Workloads, Metrics, and Guidelines. SIGMOD 2018 [pdf]
- Fotis Psallidas, Eugene Wu: Provenance in Interactive Visualizations. HILDA 2018 [pdf]
- Fotis Psallidas, Eugene Wu: Demonstration of Smoke: A Deep Breath of Data-Intensive Lineage Applications. SIGMOD (demo) 2018 [pdf]
- Fotis Psallidas, Eugene Wu: Smoke: Fine-grained Lineage at Interactive Speeds. VLDB 2018 [pdf]
- Mohamed Sarwat, Arnab Nandi: On Designing a GeoViz-Aware Database System - Challenges and Opportunities. SSTD 2017 [pdf]
- Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco Chang: Load-n-Go: Fast Approximate Join Visualizations That Improve Over Time. DSIA 2017 [pdf]
- HaoCi Zhang, Viraj Rai, Thibault Sellam, Eugene Wu: Precision Interfaces for Different Modalities. SIGMOD (demo) 2018 [pdf]
- Eugene Wu, Fotis Psallidas, Zhengjie Miao, Haoci Zhang,Laura Rettig, Yifan Wu, Thibault Sellam: Combining Design and Performance in a Data Visualization Management System. CIDR 2017 [pdf]
- Niranjan Kamat, Arnab Nandi: InfiniViz: Interactive Visual Exploration using Progressive Bin Refinement. arXiV 2017 [pdf]
- Behrooz Omidvar-Tehrani, Arnab Nandi, Nicholas Meyer, Dalton Flanagan and Seth Young: Interactive Analysis of Aviation Data (demo) ICDE 2017 [pdf]
- Meraj Ahmed Khan, Larry Xu, Arnab Nandi, Joseph Hellerstein: Data Tweening: Incremental Visualization of Data Transforms: VLDB 2017 [pdf]
- Meraj Ahmed Khan, Larry Xu, Arnab Nandi, Joseph Hellerstein: DataTweener: A Demonstration of a Tweening Engine for. Incremental Visualization of Data Transforms: VLDB 2017 demo [pdf]
- Liwen Sun, Michael J. Franklin, Jiannan Wang, Eugene Wu: Skipping-oriented Partitioning for Columnar Layouts
VLDB 2017 [pdf]
- Gabriel Ryan, Abigail Mosca, Remco Chang, Eugene Wu: Approximate Entropy as a Measure of Line Chart Complexity.
INFOVIS 2017 [Poster]
- Haoci Zhang, Thibault Sellam, Eugene Wu: Precision Interfaces. HILDA 2017 [pdf]
- Yifan Wu, Larry Xu, Remco Chang, and Eugene Wu: Towards a Bayesian Model of Data Visualization Cognition
DECISIVE 2017. [pdf]
- Eugene Wu, Arnab Nandi: Towards Perception-aware Interactive Data Visualization Systems: Data Systems for Interactive
Analysis (DSIA) Workshop 2015 [pdf]
- Juan Felipe Beltran, Ziqi Huang, Azza Abouzied, Arnab Nandi: Don’t Just Swipe Left, Tell Me Why: Enhancing Gesture-based Feedback with Reason Bins: IUI 2017 [pdf]
- Niranjan Kamat, Arnab Nandi: SESAME: A Session-Based Approach to Fast-But-Approximate Interactive Data Cube Exploration, TKDD 2017 (Special Issue on Interactive Data Exploration and Analytics). [pdf]
- Daniel Alabi, Eugene Wu: PFunk-H: Approximate Query Processing using Perceptual Models
HiLDA 2016 [pdf]
- Eugene Wu, Lilong Jiang, Larry Xu, Arnab Nandi: Graphical Perception in Animated Data Visualizations:
ArXiv [pdf]
- Eugene Wu, Leilani Battle, and Samuel R. Madden: The Case for Data Visualization Management Systems: VLDB 2014
- Prasanth Jayachandran, Niranjan Kamat, Kathik Tunga, Arnab Nandi: Combining User Interaction, Speculative Query Execution and Sampling in the DICE System: VLDB 2014
Related Code
- Crossroads: Interactive Vehicle Fleet Data Analysis using Touch Screens: Dan Arters, Trey Hakanson, Arnab Nandi> [github]
- Evaluating Interactive Data Systems: Workloads, Metrics, and Guidelines: Lilong Jiang, Protiva Rahman, Arnab Nandi [github]
Related Presentations
- PFunk-H: Approximate Query Processing using Perceptual Models
HiLDA 2016 [pdf]
Contact
Contact
Last updated: Aug 1, 2018