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Theme Lead: Shantenu Jha

Many problems at the forefront of science, engineering, medicine, and the social sciences, are increasingly complex and interdisciplinary due to the plethora of data sources and computationaal methods available today. A common feature across many of these problem domains is the amount and diversity of data and computation that must be integrated to yield insights.

For many complex data-intensive applications, moving the data may have restrictions. Increasingly important type of data-intensive applications are data-driven applications. For example, data is increasingly large-scale, distributed arising from sensors, scientific instruments & simulations. Such data-driven applications will involve computational activities triggered as a consequence of independent data creation; thus it is imperative for an application to be able to respond to unplanned changes in data load or content. Understanding how to support dynamic computations is a fundamental, but currently a critical missing element in data-intensive computing.

The 3DPAS theme seeks to understand the landscape of dynamic, distributed, data-intensive computing: the programming models and abstractions, the run-time and middleware services, and the computational infrastructure. It will analyse existing tools and services, identify missing pieces and new abstractions, and propose practical solutions and best practices.



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