The lack of tools to rapidly identify and align data from different sources is a critical, needed capability for the Department of Defense especially when it comes to automated ingestion. In the current open source Karma Mapping Tool, the Steiner tree optimization algorithm suggests semantic types during data alignment. We hypothesize that Machine Learning (ML) may perform better than the Steiner approach on a subset of column types, or “labels”, where 1.) the data is extremely similar in structure and content and 2.) inferring column type correctly is highly dependent on the interrelated components of the dataset. In this session we discuss the experimental design, our initial results, and a path toward future work in broader applications beginning with intelligence analysis in the maritime domain. The initial results from this experiment show there is promise in using ML to do column prediction in analysis environments where there are many similar or overlapping data.
Proc. SPIE. 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
KEYWORDS: Mathematical modeling, Statistical analysis, Data modeling, Visualization, Data processing, Machine learning, Analytical research, Data communications, Performance modeling, Data integration, Strategic intelligence, Military intelligence
Army Intelligence operates in a data rich environment with limited ability to operationalize exponentially increasing volumes of disparate structured and unstructured data to deliver timely, accurate, relevant, and tailored intelligence in support of mission command at echelon. The volume, velocity, variety, and veracity (the 4 Vs) of data challenge existing Army intelligence systems and processes, degrading the efficacy of the Intelligence Warfighting Function (IWfF). At the same time, industry has exploited the recent growth in data science technology to address the challenge of the 4 Vs and bring relevant data-driven insights to business leaders. To bring together the lessons from industry and the data science community, the US Army Research Laboratory (ARL) has collaborated with the US Army Intelligence Center of Excellence (USAICoE) to research these Military Intelligence (MI) challenges in an Army AR 5-5 Study entitled, “Application of Data Science within the Army Intelligence Warfighting Function.” This paper summarizes the problem statement, research performed, key findings, and way forward for MI to effectively employ data science and data scientists to reduce the burden on Army Intelligence Analysts and increase the effectiveness of data exploitation to maintain a competitive edge over our adversaries.
Army staffs at division, brigade, and battalion levels often plan for contingency operations. As such, analysts consider the impact and potential consequences of actions taken. The Army Military Decision-Making Process (MDMP) dictates identification and evaluation of possible enemy courses of action; however, non-state actors often do not exhibit the same level and consistency of planned actions that the MDMP was originally designed to anticipate. The fourth MDMP step is a particular challenge, wargaming courses of action within the context of complex social-cultural behaviors. Agent-based Modeling (ABM) and its resulting emergent behavior is a potential solution to model terrain in terms of the human domain and improve the results and rigor of the traditional wargaming process.