Research Summaries

Back Big Data and Deep Learning (BDDL) for Logistics in Support of the Fleet's Distributed Lethality Concept

Fiscal Year 2016
Division Research & Sponsored Programs
Department Naval Research Program
Investigator(s) Zhao, Ying
Sponsor NPS Naval Research Program (Navy)
Summary The Distributed lethality concept seeks to capitalize on the mobility and dispersed presence of naval forces (e.g. Surface Action Groups (SAG)) through increased lethality over an extended geographic area. Distributed lethality presents a challenge in supporting that distributed force including the challenge of logistics resources that could be potentially provided by a diverse set of countries especially in the Western Pacific.
We propose to investigate the feasibility of applying a Big Data and Deep Learning framework (BDDL) for resource management. We will work the sponsor to perform the feasibility study using a war game simulation data and sample databases from the current line of business and operations.
We seek to build a use case to apply a BDDL framework. To use this BDDL methodology, for each geographical region, the data regarding the current logistics resources, Sea Basing assets, logistics forces, partner nation supply chains as well as combat conditions will be constantly indexed, cataloged, learned and compared without required standard data attributes. The fused data will be used for search and match with real-time logistics and mission requirements. If successful, the resulted system will benefit and contribute to the fleet's distributed lethality concept greatly.
Keywords
Publications Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal