PLSA Anomaly Detection for Cellular Network Time-Series Data
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Solution Overview
Problem
Existing anomaly detection methods in cellular networks face challenges when the expected data distribution varies with time or space, leading to inaccurate results due to insufficient data per segment and neglect of time-correlation between adjacent time series points.
Innovation Solution
The implementation of a probabilistic latent semantic analysis (PLSA) with a Gaussian-based framework that models both general and specific data patterns, incorporating time and space dependencies to identify hidden relationships and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is segmented by time or space for anomaly detection, then detection specificity is improved, but data quantity per segment decreases leading to reduced detection accuracy
Solution Approach 1:
The patent combines multiple data segments across different time periods and spatial locations into a unified analysis framework. By merging data from multiple sources while maintaining segment-specific characteristics through the PLSA model, the system achieves both high detection accuracy and sufficient data quantity for reliable anomaly detection.
Solution Approach 2:
The patent introduces latent semantic dimensions through PLSA that transcend traditional time and space segmentation. By transforming data into a latent semantic space, the system can analyze patterns across multiple segments simultaneously, effectively adding a dimensional transformation that resolves the contradiction between segmentation specificity and data quantity.
2Measurement precision
If traditional anomaly detection methods are used without considering time-correlation, then computational complexity is reduced, but detection accuracy deteriorates due to neglect of time-dependent patterns
Solution Approach 1:
The patent transforms the problem by changing parameters from raw time-series data to latent semantic variables through PLSA. This parameter transformation captures time-correlation patterns while maintaining computational efficiency, as the latent space representation compresses temporal dependencies into manageable dimensions that can be analyzed without excessive computational burden.
3Measurement precision
If PLSA with time-correlation modeling is implemented, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary dimensionality reduction and pattern extraction through PLSA before conducting anomaly detection. By pre-processing the data to establish latent semantic relationships and time-correlation structures in advance, the system reduces the computational burden of the actual anomaly detection process, achieving high accuracy without excessive real-time computational complexity.
Data Source
AI summary
An anomaly detection apparatus, method, and computer program product are provided using a probabilistic latent semantic analysis (PLSA). In use, data is received, and a PLSA is performed, based on the data. Further, one or more anomalies are detected in the data, based on the PLSA. Still yet, information identifying the one or more anomalies is stored and/or displayed.