KI-Glossar
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Stream Anomaly Detection
Technique for identifying abnormal patterns in continuous data streams without requiring complete storage, using adaptive algorithms to process data in real-time.
Real-time Feature Extraction
Process of computing relevant features on the fly on incoming data to feed anomaly detection algorithms with minimal latency.
Adaptive Thresholding
Dynamic thresholding technique that automatically adjusts detection limits based on the evolution of statistical characteristics of the data stream.
Time Series Anomaly
Significant deviation in temporal patterns, trends, or seasonality of a continuous time series detected by statistical analysis or ML.
Hotspot Detection
Identification of spatio-temporal regions in the stream where the density of anomalies or the intensity of deviations significantly exceeds expected values.
Burst Detection
Detection of sudden and concentrated activity spikes in the data stream, often indicators of anomalies or exceptional events requiring immediate attention.
Adaptive Sliding Window
Variant of the sliding window whose size dynamically adjusts according to the volatility or flow rate of the stream to optimize anomaly detection.
Stream Processing Engine
Software infrastructure optimized for distributed and parallel processing of high-velocity data streams with latency and reliability guarantees.
Virtual Sliding Window
Memory-efficient implementation of sliding window using compressed data structures or sampling to process long time horizons.
Lightweight Anomaly Detection
Optimized algorithms for strict computational constraints, often based on simple statistics or lightweight probabilistic models for edge environments.
Continuous Monitoring
Persistent monitoring of data stream with real-time alerts and performance metrics to maintain detection system efficiency.
Drift Detection Method
Specific algorithm to automatically identify distribution changes in the stream and trigger necessary adaptations of the detection model.