Matrix computations (3rd ed.)
Regularization by Truncated Total Least Squares
SIAM Journal on Scientific Computing
Theoretical Computer Science
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Multi-resolution Abnormal Trace Detection Using Varied-length N-grams and Automata
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Magpie: online modelling and performance-aware systems
HOTOS'03 Proceedings of the 9th conference on Hot Topics in Operating Systems - Volume 9
Discovering Likely Invariants of Distributed Transaction Systems for Autonomic System Management
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
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It is a major challenge to process the high dimensional measurements for failure detection and localization in large scale computing systems. However, it is observed that in information systems those measurements are usually located in a low dimensional structure that is embedded in the high dimensional space. From this perspective, a novel approach is proposed in this paper to model the geometry of underlying data generation and detect anomalies based on that model. We consider both linear and nonlinear data generation models. Two statistics, the Hotelling $T^2$ and the squared prediction error ($SPE$), are used to reflect data variations within and outside the model. We track the probabilistic density of extracted statistics to monitor the system's health. After a failure has been detected, a localization process is also proposed to find the most suspicious attributes related to the failure. Experimental results on both synthetic data and a real e-commerce application demonstrate the effectiveness of our approach in detecting and localizing failures in computing systems.