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See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://
www.cs.ox.ac.uk/david.johnson/wisc11/ for details
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://
www.cs.ox.ac.uk/david.johnson/wisc11/ for details
See http://www.scalalife.eu/content/ieee-escience-2011-
workshop-computing-advances-life-science for details
See http://www.ci.uch
icago.edu/MESR/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://www.c
i.uchicago.edu/D3Science/ for details
See http://www.scalalife.eu/content/ieee-escience-2011-
workshop-computing-advances-life-science for details
See http:/
/www.computationalscience.nl/dmc2011/ for details.
See http://www.ci.uch
icago.edu/MESR/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://www.c
i.uchicago.edu/D3Science/ for details
See http://www.scalalife.eu/content/ieee-escience-2011-
workshop-computing-advances-life-science for details
See http:/
/www.computationalscience.nl/dmc2011/ for details.
See http://www.ci.uch
icago.edu/MESR/ for details.
Much scientific research and innovation requires a wide
range of data intensive computations--including high
performance computing (HPC)--to be run as part of a
complex workflow, for example with different steps for data
access, data acquisition, data transfer, data processing and
compute-intensive simulation. To simplify the process for
the user, we can orchestrate these steps using a workflow
manager and provide seamless access to remote resources
using audited credential delegation and the application
hosting environment. This talk will outline several
biomedical applications our group has been working on to
enable better medical/clinicial treatments which draw on
the use of HPC inter alia. These include patient specific HIV
drug therapy, personalized treatment of aneurysms in the
brain and patient specific cancer therapy. In this talk I will
describe the e-Science techniques used in each project and
will make a case for an integrated computational
infrastructure (data storage, networks and computational
resources) to ensure the successful development of future
biomedical applications. I will also provide an overview of the
developments at the EU level to further computational
biomedicine through the FP7 Virtual Physiological Human
(VPH) Initiative. VPH projects in which we are involved
include VPH-SHARE, which aims to provide robust cloud
based infrastructure for translational biomedical research
and clinical usage, and p-medicine, where we process large
amounts of federated medical data from different sources to
provide personalized clinical decision support. Such scenarios
require a heterogeneous computational infrastructure,
comprising secure resources from the desktop (within a
hospital) to the very largest supercomputers available
nationally and internationally, in new and disruptive ways,
for example to deliver urgent results on demand.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
For more information see the workshop website.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
For more information see the workshop website.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
The talk will review the origins of the eScience initiative
starting with the John Taylor’s ambitious £250M program in
the UK. One strand of the eScience agenda concerns data-
intensive science and ‘big data’. In Europe and the UK, and
also globally, the particle physicists used complex
middleware to build a grid of centers to move data from
CERN and to share data and computing resources for the
analysis of the LHC experiments. Other scientific
communities also have big data challenges: Jim Gray and
Alex Szalay’s pioneering work with the Sloan Digital Sky
Survey and their creation of the SkyServer Database were
major landmarks for ‘big data’ astronomy. The global
astronomy community also came together to create ‘Virtual
Observatories’ as a forum for collaboration and exchange of
data.
Both particle physics and astronomy have significant
amounts of data yet do not present the same challenge for
discovery and insight that is needed for the analysis of
genetic and bioinformatics data. There, the goal is to extract
new knowledge from very disparate types of data ranging
from gene sequences to 3-D protein structures. Similar
remarks can be made about biomedical data where
understanding features in medical images and integrating
this information with many other types of medical data is a
major challenge. In these last two examples, computer
science technologies such as Machine Learning and
Computer Vision clearly have a key role to play. Finally the
increasing deployments of sensor networks and the use of
satellite imagery are transforming many areas of
environmental science. In all these cases there is a need to
use advanced IT to assist scientists in managing, visualizing
and analyzing their data.
The eScience agenda is not only about very big data in the
Terabyte and Petabyte range. The need to collaborate, re-
use and mine many small data sets is a common feature of
many different fields and eScience covers the tools and
technologies required to make this possible. The tools must
cover the entire data life cycle, from acquisition to archive.
Furthermore, the tools needed by scientists can incorporate
advanced computer science algorithms but they also need
to be robust and reliable - not just research prototypes
beloved by computer science researchers!
Increasingly eScience technologies will be relevant to the
Humanities and Social Sciences and perhaps the term
eResearch, as in the Australian eResearch program, is a
more appropriate name. The explosive growth in scientific
data will also affect scholarly publishing and libraries. In
addition to the scientific data revolution we are also seeing a
transformation in how we publish scientific data and how
we assign credit for such tasks as data curation.
After a brief survey of the state of eScience today with
some examples of what Jim Gray called the ‘Fourth
Paradigm’ for scientific research, the talk concludes with a
look to the future where semantic computing technologies
and Cloud services are certain to play an increasingly
important role.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
For more information see the workshop website.
See http://research.microsoft.com/en-
us/events/escience2011/ for details.
For more information see the workshop website.
e-Science and activities that build on e-infrastructure has
emerged on the scientific arena the last ten years or so. It
is continuing to grow in an ever increasing pace and is
gaining more and more impact on both science and society.
The underpinning of this development is the availability and
robustness of the underlying infrastructures and the ability,
often seeded by EU level project funding, to bring large scale
collaborative teams together to pursue science issue son
the infrastructures.
There are a number of challenges for a new community to
access e-infrastructures. Some of the challenges are purely
technical – the tools of the new community need to be
adapted to the infrastructure. Other challenges come from
new computational or technical requirements introduced to
the infrastructure pushing it into new realms of operation or
access paradigms. Bridging the societal differences of
individuals of sometimes very different backgrounds both
technical and cultural is a harder challenge than often is
appreciated.
A lot of these challenges will be polarized as larger scale
projects with strong e-Science and e-Infrastructure
components are starting up in Europe. We will look at these
issues taken input from our experience in bringing the
fusion community closer to the e-science and e-
infrastructure activities in Europe.
Having passed through the empirical/observational, the
theoretical/experimental, and the computational paradigms,
science is now conducted predominantly following the data
exploration paradigm. Data is the key in modern scientific
endeavor! Incredible amounts of it of great complexity is
generated, which is then analyzed in an automatic or semi-
automatic fashion; this results in identification of common
patterns and trends or rare phenomena, which often
constitute new scientific discoveries or lead to those. In this
talk, we will present several contemporary scientific efforts
of major importance that are critically dependent on data
exploration. We will also outline some key relevant data-
management challenges that arise in the context of these
efforts and require significant advances in current
technology. At the end of the talk, it should be obvious that
the "e-Science 2012" Conference should be renamed into
"d-Science 2012"!
For more information see https://www.eg
i.eu/indico/event/653
For more details see the workshop
homepage.
For more details see the workshop
homepage.
For more details see the workshop
homepage.