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Data-intensive computing
Systems and problems that include huge data volumes and complex patterns of integration and interaction.
A better understanding of the ground beneath our feet will result from research by seismologists and Rapid—a group of computer scientists at the University of Edinburgh. The Earth's structure controls how earthquakes travel and the damage they can cause. A clear picture of this structure would be extremely valuable to earthquake planners, but it requires the analysis of huge amounts of data. The Rapid team developed a system that performs the seismologists' data-crunching, and have made it easy to use by relying on an interface familiar to all scientists – a web browser.
Below a screencast where Rapid was used to develop a portal for the UK-national academic supercomputer HECToR. The portal shows how to setup an advanced compute job involving computational chemistry. You need Flash installed in the browser to watch the video below. Click here for a large version
Modern scientific collaborations have opened up the opportunity of solving complex problems that involve multi- disciplinary expertise and large-scale computational experiments. These experiments usually involve large amounts of data that are located in distributed data repositories running various software systems, and managed by different organisations. A common strategy to make the experiments more manageable is executing the processing steps as a workflow.
Date and time:
Tuesday, 22 June, 2010 - 11:30
Location:
The Third International Workshop on Data Intensive Distributed Computing, Chicago, Illinois, US
The inherent limits to the predictability of brittle failure events such as earthquakes and volcanic eruptions are important, unknown, and much debated. We will establish techniques to determine what this limit is in the ideal case of controlled laboratory tests, for the first time in real-time, prospective mode, meaning before failure has occurred.
It is evident that data-intensive research is transforming computing landscape. We are facing the challenge of handling the deluge of data generated by sensors and modern instruments that are widely used in all domains. The number of sources of data is increasing, while, at the same time, the diversity, complexity and scale of these data resources are also growing dramatically. To survive the data tsunami, we need to improve our apparatus for the exploration and exploitation of the growing wealth of data.
Date and time:
Tuesday, 29 June, 2010 - 11:00
Location:
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, US.
It is evident that data-intensive research is transforming computing landscape. We are facing the challenge of handling the deluge of data generated by sensors and modern instruments that are widely used in all domains. The number of sources of data is increasing, while, at the same time, the diversity, complexity and scale of these data resources are also growing dramatically. To survive the data tsunami, we need to improve our apparatus for the exploration and exploitation of the growing wealth of data.
Date and time:
Thursday, 1 July, 2010 - 13:00
Location:
Computation Institute, University of Chicago, Chicago, Illinois, US.
Edinburgh Data-Intensive Research Data-intensive refers to huge volumes of data, complex patterns of data integration and analysis, and intricate interactions between data and users. Current methods and tools are failing to address data-intensive challenges effectively. They fail for several reasons, all of which are aspects of scalability.
Date and time:
Tuesday, 1 June, 2010 - 14:00
Location:
iDEA lab bio-medical data day, Informatics Forum, Edinburgh, UK
Over the last few years we have been developing a graphical language, the modified Edinburgh Pathway Notation scheme (mEPN), with which to logically depict the biological interactions that together make up pathways (1-3). We have used this language to construct a number of large-scale pathways associated with immune signaling and effector systems and have been exploring ways to model the activity of these networks.
Modern cell and developmental biology and the now-established domain of systems biology use quantitative imaging methods to measure the location, dynamics and interaction of molecules in fixed and living cells, and at increasingly high spatial and temporal resolution. Quantitative imaging depends on the development, delivery, and use of sophisticated image processing and analysis algorithms. The availability of these data analysis tools is commonly cited as a major bottleneck in scientific discovery.
The objective of this JISC-funded pilot project was to remove perceived barriers to uptake of an application that performs analysis of seismic waveform data. The aim was to provide the seismological community with a simplified system that overcame important barriers such as installation and understanding of the analysis package, location and transfer of large amounts of input data and visualisation of results.
Date and time:
Tuesday, 4 May, 2010 - 10:30
Location:
European Geosciences Union, General Assembly 2010, Vienna, Austria
Global and regional seismology monitoring systems are continuously operated and are transmitting a growing wealth of seismological data in Europe and from around the world. This opens exciting opportunities for a large range of geophysical research. The multi-use nature of these data puts a great premium on open-access data archive infrastructures that are well integrated in the European Plate Observing System (EPOS)—an ESFRI initiative of the solid earth community.