Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem
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Authors
Kendall, Tony
Zhao, Ying
Schwamm, Riqui
Subjects
lexical link analysis (LLA)
named entity extraction (NEE)
parts of speech tagging
POS
spaCy
semantic network analysis (SNA)
centrality measures
unsupervised machine learning
transformers
Program Objectives Memorandum (POM)
Program Budget Information System (PBIS)
named entity extraction (NEE)
parts of speech tagging
POS
spaCy
semantic network analysis (SNA)
centrality measures
unsupervised machine learning
transformers
Program Objectives Memorandum (POM)
Program Budget Information System (PBIS)
Advisors
Date of Issue
2022
Date
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
The US Navy systems may have unexpected significant cost growth for many reasons. The Office ofthe Chief of Naval Operations (OPNAV) manually and periodically reviews big data (structured and unstructured data) that were created within the Department of Defense requirements process to identify the programs that create excessive cost or cost growth. This research explores two questions:
1. What are the common elements of requirements that create excessive cost growth in Navy systems?
2. Assuming the elements are identified, what is the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs?
We applied classic data sciences and business intelligence tools towards a more advanced artificial general intelligence framework to analyze structured and unstructured data and identify elements and factors that create excessive cost growth. We found patterns and deep causes for high cost or cost growth programs using lexical link analysis (LLA; Zhao & Zhou, 2014), natural language processing (NLP) tools, a semantic network analyzer, anomaly detection, and causal learning concepts (Pearl, 2018; Pearl & Mackenzie, 2018). Programs with anomalous characteristics can lead to high costs or high growth. These tools provide counterfactual and drill-down discovery of the key words that explain the deep causes of cost growth. The recommendations are to apply these tools for the total benefits of analyzing Navy programs and requirements of post mortem data, towards modernizing the OPNAV’s Program Budget Information System (PBIS) to become a knowledge system that can effectively learn from historical data to make better risk predictions and decisions for the future Program Objectives Memorandum (POM).
Type
Poster
Description
NPS NRP Project Poster
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
N8 - Integration of Capabilities & Resources
Funder
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098).
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.