Colvin Run Networks, LLC presents the Maritime Agile Intelligent Data Exploitation Network (MAIDEN), extending and integrating leading commercial off-the-shelf and open source analytical tools to support mission objectives with enhanced situational awareness. MAIDEN is built on a scalable and highly flexible analytics architecture supplemented with proven iterative data mining techniques to unlock the potential from large volumes of disparate data. The Phase I project will demonstrate Big Data analysis methods leveraging modern cross-source data techniques, including those pioneered in socio-technological network science, and the latest data visualization and predictive analytics best practices with a sustainable data management strategy. MAIDEN enables (1) cross-source correlations and searches using a single query across databases, (2) machine learning-enhanced target resolution and disambiguation, (3) a scalable virtualization infrastructure, (4) rapid automated multi-source data processing, and (5) tailored visualization tools to aid analysts in determining target patterns of life while highlighting anomalies across available information sources. Implementing MAIDEN will enable mission commanders to exploit more of the existing P-8A information to make real-time and informed decisions in a maritime operational environment.
Benefit: In MAIDEN, the Navy will have a robust Big Data analytics platform to curate cross-source data while delivering powerful and flexible predictive analytics dashboards to supplement mission commanders situational awareness for critical decision making. Furthermore, MAIDEN is delivered on a microservices infrastructure, decoupling functionality from dependencies on user interfaces and back-end systems of record, for an API-based solution that will remain reliable and modular to align with mission priorities. Colvin Run has a growing line of business in Big Data analytics and emerging technology implementations with both defense and commercial customers. The MAIDEN solution fits into each of these growing business portfolios, with dual-use applications of its data architecture and the accompanying agile implementation strategy. Data discovery and human-machine teaming is a priority across the US Department of Defense, and several Commercial Big Data operations require rapid data analysis, matching, and correlation across multiple sources with applications in shipping, manufacturing, and commerce.
Keywords: Machine Learning, Machine Learning, Disparate Sources, Big Data, Computation, Decision Making, Analytics, Visualization, Automation
