Data farming

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Data Farming is the process of using a high performance computer or computing grid to run a simulation thousands or millions of times across a large parameter and value space. The result of Data Farming is a “landscape” of output that can be analyzed for trends, anomalies, and insights in multiple parameter dimensions.

1 Origins of the term
2 Usage
3 Workshops
4 External links

[edit] Origins of the term

The term Data Farming comes from the idea of planting data in the simulation and parameter/value space, and then harvesting the data that results from the simulation runs.

[edit] Usage

Data Farming was originally used in the Marine Corp’s Project Albert. Small agent-based distillation models (simulations) were created to capture a specific military challenge. These models were run thousands or millions of times at the Maui High Performance Computer Center and other facilities. Project Albert analysts would work with the military subject matter experts to refine the models and interpret the results. The Naval Post Graduate School also worked closely with Project Albert in model generation, output analysis, and the creation of new experimental designs to better leverage the computing capabilities at Maui and other facilities.

[edit] Workshops

International Data Farming Workshop 13 was held in the Netherlands in November 2006. Nine teams of scientists and subject matter experts used Data Farming techniques to explore questions in their areas which included peace support operations, net-centric operations, information sharing, ground swarm robotics, system of systems test planning, automated red teaming in urban operations, and combat identification. International Data Farming Workshop 14 took place on March 2007 in Monterey, California, USA, sponsored by the SEED Center for Data Farming at the Naval Postgraduate School.