The Operational Safety Risk Indicator (OSRI) project, led by Commander, Naval Surface Forces (CNSF), seeks to aggregate disparate data sources across the Navy into DoN Jupiter, which is the Navy’s enterprise data environment enclave of Advana, and deploy predictive risk analytics/modeling to identify significant risk factors and enable better decision-making across all echelons of leadership. By leveraging advanced machine learning and artificial intelligence methodologies, coupled with robust data visualization tools, the OSRI project seeks to enhance operational safety and readiness across the Surface Force. 
Although the there is an abundance of relevant enterprise data to serve the OSRI project, the team has tapped into one of the most pertinent and relevant data sources available to date: Tactical Messages. These messages provide a wealth of information across many naval operations, including logistics, operations, personnel, medical, and maintenance. One such form of information within Tactical Messages that directly relates to operational safety risk indicators is destructive behaviors data. Currently this information is manually extracted in multiple ways from an enormous amount of Tactical Message traffic. How this information is extracted also depends on the command or operational function, which creates data management concerns. In addition, Tactical Messages vary widely in format, are largely unstructured, and may contain errors due to manual entry. Not only does the current data aggregation process result in redundancies and potential information loss, but it inhibits the ability to make real-time decisions and analysis, thus potentially impacting the Navy’s operational safety risk posture. As a result, the OSRI team developed a machine learning application that will enable faster processing and more timely delivery of insights drawn from a wealth of unstructured data. This application takes the raw Tactical Message data as input and—using Natural Language Processing techniques—classifies the type of destructive behaviors incident. This classifier provides a predictive capability that not only enables robust analytics in a streamlined fashion, but also helps to identify the factors that contribute to the occurrence of these events. Through timely delivery of this actionable information to leadership, the Navy can make better informed decisions that enact effective counter measures against destructive behaviors. The long-term gain for such a system is enhanced operational safety and improved performance outcomes.

Operational Safety Risk Indicators


**Title:** Natural Language Processing and Data Science

**Moderator:** Jeffrey Bennett, Naval Information Warfare Center Pacific

               
        


Natural Language Processing and Data Science - Q & A

technical paper

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Jillian Jarrett

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