Improving U.S. Navy Campaign Analyses (海軍戰役分析) with Big Data

Brian L. Morgan, Harrison C. Schramm, Jerry R. Smith, Jr., Thomas W. Lucas, Mary L. McDonald, Paul J. Sanchez, Susan M. Sanchez, Stephen C. Upton, Improving U.S. Navy Campaign Analyses with Big Data, Interfaces, Volume 48, Issue 2, March-April 2018, pp. 130–146.

Decisions and investments made today determine the assets and capabilities of the U.S. Navy for decades to come. The nation has many options about how best to equip, organize, supply, maintain, train, and employ our naval forces. These decisions involve large sums of money and impact our national security. Navy leadership uses simulation-based campaign analysis to measure risk for these investment options. Campaign simulations, such as the Synthetic Theater Operations Research Model (STORM), are complex models that generate enormous amounts of data. Finding causal threads and consistent trends within campaign analysis is inherently a big data problem. We outline the business and technical approach used to quantify the various investment risks for senior decision makers. Specifically, we present the managerial approach and controls used to generate studies that withstand scrutiny and maintain a strict study timeline. We then describe STORMMiner, a suite of automated postprocessing tools developed to support campaign analysis, and provide illustrative results from a notional STORM training scenario. This new approach has yielded tangible benefits. It substantially reduces the time and cost of campaign analysis studies, reveals insights that were previously difficult for analysts to detect, and improves the testing and vetting of the study. Consequently, the resulting risk assessment and recommendations are more useful to leadership. The managerial approach has also improved cooperation and coordination between the Navy and its analytic partners.

Implementation:

STORMMiner is composed of routines built in a combination of Scala (http://www.scala-lang.org) and R (https://www.r-project.org).... STORMMiner can be instantiated on a desktop or laptop running the Linux operating system. It typically has a runtime of one to three hours, based on the number of key metrics selected for analysis, and generates approximately 50 GB of output.

Some of the new capabilities that STORMMiner provides include the following:
• Dynamic sample-size requirement determination with early-termination option;
• A quick-look dashboard;
• Time-series plots, histograms, killer-victim scoreboards, and summary statistics and indications of outliers for losses and key metrics;
• Unit and event execution graphs;
• Cluster analysis to highlight common characteristics shared by bifurcated results (if present);
• Campaign progression and event heatmaps that indicate the status of resources and campaign objectives over time and the location of casualty occurrences; and
• Classification and regression trees that identify patterns in key outcomes as a function of scenario inputs and events.