Generalized Query Planner for Distribute Fusion

Our military"s ability to dominate the battlespace depends on the ability to first"see"the battlespacethat is, for our military commanders to have awareness of the environment, entities, activities, and intent of both our adversaries and ourselves. The things we don"t control must be observed, interpreted, and analyzed to understand a situation and predict potential outcomes."Observations"come from the sensors our expeditionary, afloat, and air forces bring with them into contested areas; as well as from cooperating services and agencies with remote sensors, data centers, and analytical capabilities. In contested areas, the sensor capabilities will likely overwhelm our ability to centralize information. We must move analytics closer to the sensors, while still providing the commander a fused view of the battlespace. We propose a new Fusion Framework that leverages Harmonia"s technology for distributing MapReduce jobs across geographically distributed clusters, and our partner CUBRC"s extensive applied research in fusion to create the Generalized Query Planner for Distributed Fusion (GQPDF). The GQPDF query planner will use both shared and machine-learned knowledge of data localization to route analytical processes (e.g., MapReduce) to the best combination of tactical or enterprise fusion-data nodes, as one-time queries, or standing subscriptions for continuous awareness.