The Seventh International Workshop on Data Intensive Distributed Computing (DIDC 2016) will be held in conjunction with the 25th International Symposium on High Performance Distributed Computing (HPDC 2016), in Kyoto, Japan in June 1, 2016.

*** DIDC 2016 Workshop Program is now available! ***

The data needs of scientific as well as commercial applications from a diverse range of fields have been increasing exponentially over the recent years. This increase in the demand for large-scale data processing has necessitated collaboration and sharing of data collections among the world's leading education, research, and industrial institutions and use of distributed resources owned by collaborating parties. In a widely distributed environment, data is often not locally accessible and has thus to be remotely retrieved and stored. While traditional distributed systems work well for computation that requires limited data handling, they may fail in unexpected ways when the computation accesses, creates, and moves large amounts of data especially over wide-area networks. Further, data accessed and created is often poorly described, lacking both metadata and provenance. Scientists, researchers, and application developers are often forced to solve basic data-handling issues, such as physically locating data, how to access it, and/or how to move it to visualization and/or compute resources for further analysis.

This workshop will focus on the challenges imposed by data-intensive applications on distributed systems, and on the different state-of-the-art solutions proposed to overcome these challenges. It will bring together the collaborative and distributed computing community and the data management community in an effort to generate productive conversations on the planning, management, and scheduling of data handling tasks and data storage resources.

Topics of interest include, but are not limited to:

  • Data-intensive applications and their challenges
  • Data clouds, data grids, and data centers
  • New architectures for data-intenstive computing
  • Data virtualization, interoperability, and federation
  • Data-aware toolkits and middleware
  • Dynamic data-driven science
  • Data collection, provenance, and metadata
  • Network support for data-intensive computing
  • Remote and distributed visualization of large scale data
  • Data archives, digital libraries, and preservation
  • Service oriented architectures for data-intensive computing
  • Data privacy and protection in a collaborative environment
  • Peer-to-peer data movement and data streaming
  • Scientific breakthrough enabled by DIDC
  • Future research challenges in data-intensive computing
  • Energy-efficient data-intensive systems
  • New programming models for data-intensive computing