Vertical Integration of Information for Battlespace Awareness and Prediction: A Big Data Analytics Approach
Abstract
Vertical Integration of Information for Battlespace Awareness and Prediction: A Big Data Analytics ApproachIn modern warfare, Battlespace Awareness and Prediction (BAP) are critical for mission success.However, because of the large heterogeneous datasets such as nautical charts, ocean conditions, massive time series data collected by various sensor systems, BAP is a monumental task for the data analysts and commanders. In order to address this challenge, a multidisciplinary team of researchers (PI: Lijun Qian, Co PIs: Lei Huang, Pamela Obiomon, YonghuiWang, Jianren Zhou) from Prairie View A&M University propose a proof-of-concept project on vertical integration ofinformation for battlespace awareness and prediction using a big data analytics approach. This project seeks novel methods to integrate and exploit massive datasets from air, surface, and underwater sources for better understanding of the battlespace situation and prediction. The proposed project will leverage the team s data analysis expertise, and existing infrastructuresuch as the High Performance Computing facility and the Cloud Computing facility. Ample efforts will be carried out to develop new components in the curriculum to provide students with the state-of-the-art knowledge in big data analytics and mentor them to gain hands-onexperiences. The proposed project will build new partnerships with government agencies and industrial enterprises to focus on research of the big data challenge and educating and training data scientists and engineers who will become the workforce in the future data-centric economy and contribute to DOD missions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2017
- Source ID
- N000141713062
Entities
People
- Lijun Qian
Organizations
- Office of Naval Research
- Prairie View State College
- United States Navy