Pattern of Life Analysis Framework for Multidimensional Data
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Solution Overview
Problem
Current data science applications lack the ability to effectively model and detect anomalies in multidimensional data sets containing both categorical and non-categorical data in a simultaneous manner, which is crucial for pattern of life estimation and anomaly detection in mission-directed contexts.
Innovation Solution
A method and framework that processes both categorical and non-categorical data using kernel density estimation and normal distribution models to identify anomalies by assigning dimension precision values and determining anomalous data items based on statistical models, allowing for real-time pattern of life estimation and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data science applications use traditional analytics methods for text, imagery, and numerical data, then pattern classification and time-series trending can be achieved, but the ability to simultaneously model and detect anomalies in multidimensional data sets containing both categorical and non-categorical data is lacking
Solution Approach 1:
The system segments the multidimensional data set into distinct categorical and non-categorical data groups, applying specialized statistical models to each type. Categorical data is processed using normal distribution models, while non-categorical data uses kernel density estimation, allowing each data type to be handled with appropriate methods without overwhelming system complexity
Solution Approach 2:
The framework creates a universal anomaly detection system that can simultaneously process multiple data types (categorical, numerical, spatial) through a unified architecture. The system uses a common anomaly scoring mechanism based on statistical model comparisons that works across all data types, providing multi-functionality without requiring separate specialized systems for each data type
2Measurement precision
If the system processes both categorical and non-categorical data simultaneously, then pattern of life estimation accuracy improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The system changes the statistical parameters used for modeling based on data type: using normal distribution parameters (mean, variance) for categorical data and kernel density parameters for non-categorical data. This parameter adaptation allows accurate pattern of life estimation for each data type while optimizing computational requirements through appropriate mathematical choices
3Productivity
If traditional data-driven analytics are used without context-defined focus, then general pattern classification is achieved, but mission-directed anomaly detection capability is insufficient
Solution Approach 1:
The system implements feedback through iterative pattern of life modeling where historical data establishes baseline behavioral patterns, which then serve as reference models for detecting anomalies in new data. The anomaly detection results feed back into refining the pattern models, creating a continuous improvement loop that enhances both detection efficiency and mission-directed reliability
Data Source
AI summary
In accordance with various embodiments of the disclosed subject matter, a method and framework configured for modeling a pattern of life (POL) by processing both categorical data and non-categorical data (e.g., numeric, spatial etc.), conducting pattern of life estimation (POLE), and detecting anomalous data in a multi-dimensional data set in a substantially simultaneous manner by comparing statistical PoL results.