Communications - October 2010

last;byte
DOI: 10.1145/1831407.1831432
Leah Hoffmann
Q&a
Gray’s Paradigm
Tony Hey talks about Jim Gray and his vision
of a new era of collaborative, data-intensive science.

tOnY he Y, vice PreSiDent of the External Research Division of Microsoft Research, has long straddled the scientific and computational worlds. Hey began his career as a particle physicist at the University of Southampton before changing fields and serving as head of its School of Electronics and Computer Science. Prior to his appointment at Microsoft, Hey served as director of the United Kingdom’s e-Science Program, where he worked to develop technologies to enable collaborative, multidisciplinary, and data-intensive science. Here, he talks about a book of essays he co-authored, The Fourth Paradigm, which commemorates the work of his late colleague Jim Gray and points the way to a new era of scientific collaboration.

the title of your book, The Fourth Par-
adigm, refers to the idea that we need
new tools to cope with the explosion of
data in the experimental sciences.

Jim Gray’s insight was that experimental science and theoretical science have been with us since Newton, and over the last 50 years, computational science has matured as a methodology for scientific research. Jim thought that we are now seeing the emergence of a fourth paradigm for scientific research, namely data-intensive science. For this, researchers need a different set of skills from those required for experimental, theoretical, and computational science.

tony hey speaking at the ninth annual microsoft Research faculty Summit, which brought together 400 academics from 150 universities across five continents.

data cleansing, data visualization, and how relational databases work. The new data-intensive research paradigm does not replace the other ones—it’s quite clear that data-intensive science uses both theory and computation.

Different skill sets such as?

For data-intensive science, researchers need a totally new set of skills such as an understanding of data mining,

how did you come to be involved in
this line of research?

I first met Jim Gray in 2001, when I was running the U.K.’s e-Science Program. In discussions with Jim over the next five years, I came to agree with his view that the computer science community can really make a difference to scientists who are trying to solve difficult problems.

And that’s an idea you carry on in your
work with Microsoft?

Indeed. Computer science has powerful technologies it can offer scientists, but also things it can learn from tackling some of the difficult scientific challenges. So I really have a wonderful job, working both with great computer scientists and with great scientists.