Context Data Layer for OBP and Knowledge Model Integration
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Solution Overview
Problem
The Intelligence Community faces challenges in transforming vast amounts of raw data into actionable intelligence in a timely and cost-effective manner due to the 'Four V's (Variety, Volume, Velocity, and Veracity) within stove-piped systems, leading to inefficiencies and potential gaps in information retrieval.
Innovation Solution
Object-Based Production (OBP) creates conceptual objects for people, places, and things to consolidate information and intelligence, combined with Knowledge Modeling (KM) to automatically connect new data with existing models, leveraging Linked Data Façades (LDFs) for standardized data communication and a Linked Data Knowledge Graph (LDKG) for integrated analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts manually sift through data in stove-piped systems, then they can retrieve information from individual repositories, but the process is time-consuming and prone to missing important information
Solution Approach 1:
The patent merges multiple stove-piped data repositories into a unified data lake, consolidating information from various sources into a single accessible location. This eliminates the need for analysts to manually navigate multiple separate systems, thereby reducing time loss while maintaining comprehensive information retrieval through a unified interface.
Solution Approach 2:
The patent introduces an intermediary layer consisting of data lakes and knowledge graphs that mediate between raw data repositories and analysts. This intermediary structure automatically organizes and connects data from multiple sources, reducing manual search time while ensuring comprehensive information retrieval through automated data linking and relationship mapping.
2Productivity
If analysts work individually in stove-piped systems, then they can process data within their specific repositories, but the overall productivity is insufficient given the vast amount of data
Solution Approach 1:
The patent combines individual analyst workspaces into a collaborative platform that accesses a shared data lake. This merging enables analysts to simultaneously process and analyze the same vast data volume from different angles, significantly improving overall productivity while maintaining the ability to handle large quantities of data through distributed collaborative processing.
Solution Approach 2:
The patent creates a universal data lake and knowledge graph infrastructure that serves multiple analysts and functions simultaneously. This multi-functional system allows the same data infrastructure to support various analysis tasks and collaborative workflows, improving productivity across the board while efficiently managing vast data volumes through shared resources.
3Device complexity
If traditional data organization methods are used, then data can be stored in individual repositories, but the complexity of managing multiple systems increases
Solution Approach 1:
The patent merges multiple complex individual repositories into a single unified data lake, reducing system integration complexity by eliminating the need to manage and integrate numerous separate systems. This consolidation maintains high adaptability and data sharing capability through a standardized unified interface that facilitates easy data access and exchange across the organization.
4Reliability
If more data is collected to improve intelligence quality, then actionable intelligence can be enhanced, but the cost and resource requirements become unsustainable
Solution Approach 1:
The patent extracts only the most relevant and actionable information from vast data collections using automated filtering and analysis tools. This extraction process maintains high intelligence quality by focusing on critical data points while reducing the overall processing burden, thereby lowering energy and resource consumption without sacrificing reliability.
Solution Approach 2:
The patent replaces manual data processing and analysis with automated computational systems, including machine learning algorithms and automated knowledge graph construction. This substitution maintains or enhances intelligence quality through more sophisticated automated analysis while significantly reducing the human resources and energy required for data processing.
Data Source
AI summary
Techniques, systems, architectures, and methods for efficient and accurate intelligence gathering comprising the combination of Knowledge Modeling (KM) and Object Based Production (OBP) techniques, in embodiments, leveraging a context data layer within Knowledge Models (KMs) to store connections between models and OBP objects that are representative of any entities, organizations, resources, locations, etc. described within a model.