Quantifying, Visualizing, and Tracking Capability Gaps

While there are numerous sources of information/knowledge that identify warfighting capability gaps and/or provide recommendations to close gaps and/or provide new/improved capabilities to the fleet, there is no comprehensive system, and responsible entity, that captures all that information in one place to provide a clear and concise picture of progress being made, or not made, to close identified gaps and/or provide a capability. To address this problem, we developed a methodology based on Multi Criteria Decision Analysis (MCDA) methods to calculate and visualize a capability gap score at any given point in time to depict capability gap resolution progress based on substantiated real-time information. In this effort we expand the framework used to evaluate capabilities by adding new elements and sub-elements to the framework and extend the MCDA methodology by incorporating different models for calculating the capability gap score. These models include the Weighted Sum Model (WSM), the Weighted Product Model (WPM), the Weighted Aggregated Sum Product Assessment (WASPA), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Analytic Hierarchy Process (AHP). The goal is to develop a comprehensive methodology to 1) support prioritization of capabilities based on hard data, 2) provide a clear and concise picture of progress being made, or not made, to close identified gaps and/or provide a capability, and 3) support the creation of a central repository for organizations to distribute pertinent information.Navy Warfare Development Command (NWDC)Naval Postgraduate SchoolNaval Research Program (PE 0605853N/2098)Approved for public release; distribution is unlimited

Predictive Modeling for Navy Readiness Based on Resource Investment in Supply Support and Maintenance

NPS NRP Executive SummaryThe Navy invests substantial resources to fleet maintenance in terms of part supply, corrective maintenance, maintenance availabilities, and overhauls. In order to measure and prioritize weapon systems investment decisions, an endurance supply metric (Es)is being developed to ensure these systems are ready for tasking across the full spectrum of operations. This research project will attempt to extend the endurance supply concept beyond a single ship by determining what would be the endurance supply for a given group of ships within line of sight of each other and assuming they could transfer parts from one ship to another. The effort will also determine the endurance supply for a single ship assuming resupply from the wholesale system is allowed. The research will provide guidance to what data should the Navy capture to make better decisions using the Es metric.N4 - Fleet Readiness & LogisticsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Predictive Modeling for Navy Readiness Based on Resource Investment in Supply Support and Maintenance

NPS NRP Project PosterThe Navy invests substantial resources to fleet maintenance in terms of part supply, corrective maintenance, maintenance availabilities, and overhauls. In order to measure and prioritize weapon systems investment decisions, an endurance supply metric (Es)is being developed to ensure these systems are ready for tasking across the full spectrum of operations. This research project will attempt to extend the endurance supply concept beyond a single ship by determining what would be the endurance supply for a given group of ships within line of sight of each other and assuming they could transfer parts from one ship to another. The effort will also determine the endurance supply for a single ship assuming resupply from the wholesale system is allowed. The research will provide guidance to what data should the Navy capture to make better decisions using the Es metric.N4 - Fleet Readiness & LogisticsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Predictive Modeling for Navy Readiness Based on Resource Investment in Supply Support and Maintenance

NPS NRP Technical ReportThe Navy invests substantial resources to fleet maintenance in terms of part supply, corrective maintenance, maintenance availabilities, and overhauls. In order to measure and prioritize weapon systems investment decisions, an endurance supply metric (Es)is being developed to ensure these systems are ready for tasking across the full spectrum of operations. This research project will attempt to extend the endurance supply concept beyond a single ship by determining what would be the endurance supply for a given group of ships within line of sight of each other and assuming they could transfer parts from one ship to another. The effort will also determine the endurance supply for a single ship assuming resupply from the wholesale system is allowed. The research will provide guidance to what data should the Navy capture to make better decisions using the Es metric.N4 - Fleet Readiness & LogisticsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Analysis, Design, Implementation, and Deployment of a Prototype Maintenance Advisor Expert System for the MK92 Fire Control System

17 USC 105 interim-entered record; under review.In an effort to meet the challenges presented by the fiscal realities of today's defense budget, the Department of Defense (DoD) is seeking to exploit technology that promises to decrease operating costs, while improving operational readiness. Efforts which reduce repair costs, system down time, and the reliance upon outside technical representative are of particular interest. The development of the MK92 Maintenance Advisor Expert System (MK92 MAES) is one such effort. This paper describes the design and development of the MK92 MAES for the diagnosis and repair of the MK92 MOD 2 fire control system deployed on U.S. Navy guided missile frigates. System development is presented in terms of an expert system life cycle model which includes a thorough cost/benefit analysis, a novel approach for knowledge acquisition, an implementation strategy using a visual expert system development environment, and a phased deployment strategy. The system was developed by faculty and graduate students at the Naval Postgraduate School in cooperation with the Naval Warfare Center, Port Hueneme Division.The author thanks...Dean of Research Office at the Naval Postgraduate School for paying the publication cost

Quantifying, Visualizing, and Tracking Capability Gaps

NPS NRP Project PosterWhile numerous sources of information identify warfighting capability gaps and/or provide recommendations to close gaps and/or provide new/improved capabilities to the fleet, no comprehensive system, and responsible entity, captures all of that information in one place to provide a clear and concise picture of progress being made to close identified gaps and/or provide a capability. To address this problem, we developed in a previous effort, a methodology based on Multi-Criteria Decision Analysis (MCDA) methods to calculate and visualize a capability gap score at any given point in time to depict capability gap resolution progress across the elements of the Doctrine, Organization, Training, Materiel, Leadership, Education, Personnel, and Facilities (DOTMLPF) framework and based on substantiated real-time information. In this effort we expand the DOTMLPF framework used to evaluate capabilities by adding new elements and sub-elements and extend the MCDA methodology by incorporating different models for calculating the capability gap score. These models include the Weighted Sum Model (WSM), the Weighted Product Model (WPM), the Weighted Aggregated Sum Product Assessment (WASPA), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Analytic Hierarchy Process (AHP). The goal of the effort is to develop a comprehensive methodology that would enable Navy leadership to have a clearer picture of what has been accomplished, what remains to be done, who has action, and the critical path to closing the gap and/or delivering a capability.Navy Warfare Development Command (NWDC)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Employing Machine Learning to Predict Student Aviator Performance (Continuation)

NPS NRP Executive SummaryMachine learning analysis of student aviator training performance data offers novel and more accurate methodologies for performance assessment to include identifying students for attrition or remediation as well as optimal pipeline assignments. In a previous effort, we identified important predictors and developed prediction models of performance in primary, intermediate, and advanced training based on data from ASTB, IFS, and API training. In this proposal, we extend the effort to later stages of training by developing models to predict performance in intermediate, advanced training as well as FRS based on primary training. The goal of the analysis is to: 1) determine the set of metrics predictive of student performance for these stages of training; 2) reveal trends and patterns which may indicate where and when remedial action is needed; and.3) identify which aviation pipeline a student will be most successful. The methodology proposed for this research is based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM process model includes six phases that address the main issues in data mining. The six phases are undertaken in a cyclical and iterative manner and include: Business/Mission Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. The research deliverables include a technical report detailing the application of the methodology to the identified stages of aviation training, a PowerPoint presentation, and the results of one or more statistical/machine learning models.Commander, U.S. Pacific Fleet (COMPACFLT)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Employing Machine Learning to Predict Student Aviator Performance (Continuation)

NPS NRP Project PosterMachine learning analysis of student aviator training performance data offers novel and more accurate methodologies for performance assessment to include identifying students for attrition or remediation as well as optimal pipeline assignments. In a previous effort, we identified important predictors and developed prediction models of performance in primary, intermediate, and advanced training based on data from ASTB, IFS, and API training. In this proposal, we extend the effort to later stages of training by developing models to predict performance in intermediate, advanced training as well as FRS based on primary training. The goal of the analysis is to: 1) determine the set of metrics predictive of student performance for these stages of training; 2) reveal trends and patterns which may indicate where and when remedial action is needed; and.3) identify which aviation pipeline a student will be most successful. The methodology proposed for this research is based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM process model includes six phases that address the main issues in data mining. The six phases are undertaken in a cyclical and iterative manner and include: Business/Mission Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. The research deliverables include a technical report detailing the application of the methodology to the identified stages of aviation training, a PowerPoint presentation, and the results of one or more statistical/machine learning models.Commander, U.S. Pacific Fleet (COMPACFLT)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Quantifying, Visualizing, and Tracking Capability Gaps

NPS NRP Executive SummaryWhile numerous sources of information identify warfighting capability gaps and/or provide recommendations to close gaps and/or provide new/improved capabilities to the fleet, no comprehensive system, and responsible entity, captures all of that information in one place to provide a clear and concise picture of progress being made to close identified gaps and/or provide a capability. To address this problem, we developed in a previous effort, a methodology based on Multi-Criteria Decision Analysis (MCDA) methods to calculate and visualize a capability gap score at any given point in time to depict capability gap resolution progress across the elements of the Doctrine, Organization, Training, Materiel, Leadership, Education, Personnel, and Facilities (DOTMLPF) framework and based on substantiated real-time information. In this effort we expand the DOTMLPF framework used to evaluate capabilities by adding new elements and sub-elements and extend the MCDA methodology by incorporating different models for calculating the capability gap score. These models include the Weighted Sum Model (WSM), the Weighted Product Model (WPM), the Weighted Aggregated Sum Product Assessment (WASPA), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Analytic Hierarchy Process (AHP). The goal of the effort is to develop a comprehensive methodology that would enable Navy leadership to have a clearer picture of what has been accomplished, what remains to be done, who has action, and the critical path to closing the gap and/or delivering a capability.Navy Warfare Development Command (NWDC)U.S. Fleet Forces Command (USFF)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Federated database management system: Requirements, issues and solutions

The use of database management systems (DBMS) to replace conventional file processing systems has dramatically increased in the past years. Although the use of DBMSs overcomes many of the limitations of file processing systems, many important applications require access to and integration of information among several and often incompatible DBMSs. In this paper we discuss an approach, known as the federated database approach, that allows users and applications to access and manipulate data across several heterogeneous databases while maintaining their autonomy. We discuss the requirements and objectives of a federated database management system, and outline the major issues and challenges for building and using such a system. In particular, we address the design issues from three angles: transaction management, system architecture, and schema integration. Also, we present a five-step integration methodology followed by a comprehensive example to illustrate the concepts and techniques involved in this integration methodology