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Call for papers: D3Science

Papers are solicited for a workshop on data-intensive, distributed and dynamic e-science problems.

Workshop on D3Science - Call for Papers

(To be held with IEEE e-Science 2011) Monday, 5 December 2011 Stockholm, Sweden

This workshop is interested in data-intensive, distributed, and dynamic (D3) science. It will also focus on innovative approaches for scalability in the end-to-end real-time processing of scientific data. We refer to D3 applications as those are data-intensive, are fundamentally, or need to be, distributed, and need to support and respond to data that may be non-persistent and is dynamically generated. We are also looking to bring researchers together to look at holistic, rather than piecewise, approaches to the end-to-end processing and managing of scientific data.
There has been a lot of effort in managing and distributing tasks where computation is dominant. Such applications have after all, been historically the drivers of "grid" computing. There has been, however, relatively less effort on tasks where the computationalload is matched by the data load, or even dominated by the data load. For such tasks to be able to operate at scale, there are conceptually simple run-time tradeoffs that need to be made, such as determining whether to move data to compute versus moving compute to data, or possibly regenerating data on-the-fly. Due to fluctuating resource availability and capabilities, as well as insufficient prior information about application requirements, such decisions must be made at run-time. Furthermore, resource, connectivity and/or storage constraints may require the data to be manipulated in-transit so that it is "made-right" for the consumer. Currently it is very difficult to implement these dynamic decisions or the underlying mechanisms in a general-purpose and scalable fashion. Although the increasing volumes and complexity of data will make many problems data-dominated, the computational requirements will still be high. In practice, data-intensive applications will encompass data-driven applications. For example, many data-driven applications will involve computational activities triggered as a consequence of independently created data; thus it is imperative for an application to be able to respond to unplanned changes in data load or content. Therefore, understanding how to support dynamic computations is a fundamental, but currently missing element in data-intensive computing.
The D3Science workshop builds upon a 3 year research theme on Distributed Programming Abstractions (DPA,, which has held a series of related workshops including but not limited to e-Science 2008, EuroPar 2008 and the CLADE series, and the ongoing 3DPAS ( research theme funded by the NSF and UK EPSRC, which is holding one workshop in June 2011: the 3DAPAS workshop ( The workshop is intended to lead to a funding proposal for transcontinental collaboration, with contributors as potential members of the collaboration, and as such, we are particularly interested is discussing both existing and future projects that are suitable for transcontinental collaboration.
Topics of interest include but are not limited to:
  • Case studies of development, deployment and execution of representative
  • D3 applications, particularly projects suitable for transcontinental collaboration
  • Programming systems, abstractions, and models for D3 applications
  • Discussion of the common, minimally complete, characteristics of D3 application
  • Major barriers to the development, deployment, and execution of D3 applications, and primary challenges of D3 applications at scale
  • Patterns that exist within D3 applications, and commonalities in the way such patterns are used
  • How programming models, abstraction and systems for data-intensive applications can be extended to support dynamic data applications
  • Tools, environments and programming support that exist to enable emerging distributed infrastructure to support the requirements of dynamic applications (including but not limited to streaming data and in-transit data analysis)
  • Data-intensive dynamic workflow and in-transit data manipulation
  • Adaptive/pervasive computing applications and systems
  • Abstractions and mechanisms for dynamic code deployment and "moving code to data"
  • Application drivers for end-to-end scientific data management
  • Runtime support for in-situ analysis
  • System support for high end workflows
  • Hybrid computing solutions for in-situ analysis
  • Technologies to enable multi-platform workflows

Submission instructions

Authors are invited to submit papers containing unpublished, original work (not under review elesewhere) of up to 8 pages of double column text using single spaced 10 point size on 8.5 x 11 inch pages, as per IEEE 8.5 x 11 manuscript guidelines. Templates are available: Authors should submit a PDF or PostScript (level 2) file that will print on a PostScript printer. Papers conforming to the above guidelines can be submitted through the workshop's paper submission system: It is a requirement that at least one author of each accepted paper register and attend the conference.

Important dates

  • 17 July 2011 - submission date
  • 23 August 2011 - decisions announced
  • 23 September 2011 - final versions of papers due to IEEE for proceedings


  • Daniel S. Katz, University of Chicago & Argonne National Laboratory, USA
  • Neil Chue Hong, University of Edinburgh, UK
  • Shantenu Jha, Rutgers University & Louisiana State University, USA
  • Omer Rana, Cardiff University, UK

PC members

  • Gagan Aggarwal, Ohio State University, USA
  • Deb Agarwal, Lawrence Berkeley National Lab, USA
  • Gabrielle Allen, Lousiana State University, USA
  • Malcolm Atkinson, University of Edinburgh, UK
  • Adam Barker, University of St Andrews, UK
  • Paolo Besana, University of Edinburgh, UK
  • Jon Blower, University of Reading, UK
  • Yun-He Chen-Burger, University of Edinburgh, UK
  • Simon Dobson, University of St Andrews, UK
  • Gilles Fedak, INRIA, France
  • C├ęcile Germain, University Paris Sud, France
  • Keith R. Jackson, Lawrence Berkeley National Lab, USA
  • Manish Parashar, Rutgers, USA
  • Abani Patra, University of Buffalo, USA
  • Yacine Rezgui, Cardiff University, UK
  • Yogesh Simmhan, University of Southern California, USA
  • Domenico Talia, University of Calabria, Italy
  • Paul Watson, Newcastle University, UK
  • Jon Weissman, University of Minnesota, USA

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