Visualization and Interactive Data Analysis

The increasing scale and accessibility of digital data provides an
unprecedented resource for informing research, business and public
policy. Yet acquiring and storing this data is, by itself, of little
value. Turning data into knowledge is a fundamental challenge for both
computer systems and user interface research: it requires integrating
data management systems and analysis algorithms with human judgments
of the meaning and significance of observed patterns. In this talk, I
will discuss our research attempting to address this challenge through
novel interactive systems for data visualization and manipulation.

First, visual representations are regularly used to aid perception of
patterns, trends and outliers in data. To aid this process, we are
investigating the design of declarative, domain-specific languages for
custom visualization. Our resulting languages (Protovis and D3)
simplify specification and enable performance optimization while
preserving an expressive design space. These systems are now widely
used throughout academia and industry.

Second, data analysts often expend an inordinate amount of effort
manipulating data and assessing data quality issues. With our Wrangler
system, users can construct data transformation scripts in a direct
manipulation interface. Wrangler uses programming-by-demonstration
methods to automatically suggest applicable transforms and preview
their results. The end result is not simply transformed data, but a
reusable program that can be run on other platforms (e.g., MapReduce)
to process data at scale. Once the data has been suitably transformed,
our Profiler system combines anomaly detection, scalable visual
summaries and automatic view suggestion to aid quality assessment.

Collectively, these systems contribute new approaches for improving
the efficiency and scale at which expert analysts work, while lowering
the threshold for non-experts.

Jeffrey Heer is an Assistant Professor of Computer Science at Stanford
University, where he works on human-computer interaction,
visualization and social computing. His research investigates the
perceptual, cognitive and social factors involved in making sense of
large data collections, resulting in new interactive systems for
visual analysis and communication. The visualization tools developed
by his lab (Prefuse, Flare, Protovis & D3) are used by researchers,
corporations and thousands of data enthusiasts around the world. His
group has received Best Paper and/or Honorable Mention awards at the
premier venues in Human-Computer Interaction and Information
Visualization (ACM CHI, ACM UIST, IEEE InfoVis). In 2009 Jeff was
included in MIT Technology Review's TR35; in 2012 he was named a Sloan
Foundation Research Fellow. He holds BS, MS and PhD degrees in
Computer Science from the University of California, Berkeley.