Natural language interface for data visualization debuts at prestigious IEEE conference

October 23, 2019

BROOKLYN, New York, Tuesday, October 22, 2019 - The ubiquity and sheer volume of data generated today give experts in virtually every domain ample information to track everything from financial trends, disaster evacuation routes, and street traffic, to animal migrations, weather patterns, and disease vectors. But using this data to build visualizations of complex predictive models using machine learning is a challenge to experts who lack the requisite computer science skills.

A team at the NYU Tandon School of Engineering's Visualization and Data Analytics (VIDA) lab, led by Claudio Silva, professor in the department of computer science and engineering, developed a framework called VisFlow, by which those who may not be experts in machine learning can create highly flexible data visualizations from almost any data. Furthermore, the team made it easier and more intuitive to edit these models by developing an extension of VisFlow called FlowSense, which allows users to synthesize data exploration pipelines through a natural language interface.

The research, "FlowSense: A Natural Language Interface for Visual Data Exploration with a Dataflow System" won the best-paper award at this year's IEEE Conference on Visual Analytics Science and Technology (VAST).

On Tuesday, October 22, Bowen Yu, who received his doctorate at NYU Tandon under Silva, will present the paper at the opening plenary session of the IEEE Visualization Conference (IEEE VIS) in Vancouver, British Columbia. The study is one of several papers spotlighting VIDA research that will be presented at IEEE VIS, the leading venue for visualization research and a premier conference for computer graphics.

At the conference, collaborators with VIDA, which has established itself as a leading research center for data visualization, will present visualization modeling projects with applications in astronomy, medicine, and climate research developed at or with the center: VisFlow, introduced in 2017 and funded in part by the Defense Advanced Research Projects Agency's Data Driven Discovery of Models program is a web-based framework that allows the user to use simple drag-and-drop actions to interact with data easily, letting users create visual data models based on time series, networks, geographical locations, and more, all of which can be formed into a compact and interactive visualization dashboard.

Yu said FlowSense takes these capabilities a step further. "Imagine if one could simply speak or type a sentence to activate a dataflow diagram," he said. "This capability would make non-experts more comfortable users, while providing experienced users with shortcuts. We believe that with natural language support we can mitigate the learning curve for a system like this and make dataflow more accessible" he said.

Silva, an IEEE Fellow who is affiliated with NYU's Courant Institute for Mathematical Sciences, Center for Data Science, Center for Urban Science and Progress, and Center for Advanced Technology in Telecommunications, added, "We're offering VisFlow and FlowSense as open-source, free-to-all code-based frameworks on github, as a way to motivate further development for visualization purposes. There really is a lot more research that could be done in this area, and it is my hope that FlowSense will be a major stimulant for more collaborative work in making dataflow systems more flexible, easy to use, and popular among data analysts."
This research is supported by the Moore-Sloan Data Science Environment at NYU, NASA, and the National Science Foundation. About the New York University Tandon School of Engineering

The NYU Tandon School of Engineering dates to 1854, the founding date for both the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute (widely known as Brooklyn Poly). A January 2014 merger created a comprehensive school of education and research in engineering and applied sciences, rooted in a tradition of invention and entrepreneurship and dedicated to furthering technology in service to society. In addition to its main location in Brooklyn, NYU Tandon collaborates with other schools within NYU, one of the country's foremost private research universities, and is closely connected to engineering programs at NYU Abu Dhabi and NYU Shanghai. It operates Future Labs focused on start-up businesses in downtown Manhattan and Brooklyn and an award-winning online graduate program. For more information, visit

NYU Tandon School of Engineering

Related Data Articles from Brightsurf:

Keep the data coming
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Astronomers are bulging with data
For the first time, over 250 million stars in our galaxy's bulge have been surveyed in near-ultraviolet, optical, and near-infrared light, opening the door for astronomers to reexamine key questions about the Milky Way's formation and history.

Novel method for measuring spatial dependencies turns less data into more data
Researcher makes 'little data' act big through, the application of mathematical techniques normally used for time-series, to spatial processes.

Ups and downs in COVID-19 data may be caused by data reporting practices
As data accumulates on COVID-19 cases and deaths, researchers have observed patterns of peaks and valleys that repeat on a near-weekly basis.

Data centers use less energy than you think
Using the most detailed model to date of global data center energy use, researchers found that massive efficiency gains by data centers have kept energy use roughly flat over the past decade.

Storing data in music
Researchers at ETH Zurich have developed a technique for embedding data in music and transmitting it to a smartphone.

Life data economics: calling for new models to assess the value of human data
After the collapse of the blockchain bubble a number of research organisations are developing platforms to enable individual ownership of life data and establish the data valuation and pricing models.

Geoscience data group urges all scientific disciplines to make data open and accessible
Institutions, science funders, data repositories, publishers, researchers and scientific societies from all scientific disciplines must work together to ensure all scientific data are easy to find, access and use, according to a new commentary in Nature by members of the Enabling FAIR Data Steering Committee.

Democratizing data science
MIT researchers are hoping to advance the democratization of data science with a new tool for nonstatisticians that automatically generates models for analyzing raw data.

Getting the most out of atmospheric data analysis
An international team including researchers from Kanazawa University used a new approach to analyze an atmospheric data set spanning 18 years for the investigation of new-particle formation.

Read More: Data News and Data Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to