[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Qi et al., 2017 - Google Patents

Fault detection and localization in distributed systems using recurrent convolutional neural networks

Qi et al., 2017

Document ID
1627220185899874899
Author
Qi G
Yao L
Uzunov A
Publication year
Publication venue
International Conference on Advanced Data Mining and Applications

External Links

Snippet

Early detection of faults is essential to maintaining the reliability of a distributed system. While there are many solutions for detecting faults, handling high dimensionality and uncertainty of system observations to make an accurate detection still remains a challenge …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G06N5/025—Extracting rules from data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management

Similar Documents

Publication Publication Date Title
Vinayakumar et al. Long short-term memory based operation log anomaly detection
Khan et al. Multi-gcn: Graph convolutional networks for multi-view networks, with applications to global poverty
US10354204B2 (en) Machine learning predictive labeling system
AU2019201857B2 (en) Sparse neural network based anomaly detection in multi-dimensional time series
WO2018212710A1 (en) Predictive analysis methods and systems
US20190034497A1 (en) Data2Data: Deep Learning for Time Series Representation and Retrieval
He et al. Efficiently localizing system anomalies for cloud infrastructures: a novel dynamic graph transformer based parallel framework
He et al. MTAD‐TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern
CN114386521A (en) Anomaly detection method, system, device and storage medium for time series data
Qi et al. Fault detection and localization in distributed systems using recurrent convolutional neural networks
Mohindru et al. Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
CN114091930A (en) Business indicator early warning method, device, electronic device and storage medium
CN113743675A (en) Cloud service QoS deep learning prediction model
Zhang et al. A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network
Soni et al. Deep learning approach for detection of fraudulent credit card transactions
Wang et al. Artificial intelligence of things (AIoT) data acquisition based on graph neural networks: A systematical review
Gaurav et al. Interpretability vs explainability: the black box of machine learning
Ren et al. Triple: the interpretable deep learning anomaly detection framework based on trace-metric-log of microservice
US11195084B1 (en) Neural network training system
He et al. Dynamic model interpretation-guided online active learning scheme for real-time safety assessment
Sserwadda et al. Topological similarity and centrality driven hybrid deep learning for temporal link prediction
US20210374613A1 (en) Anomaly detection in high dimensional spaces using tensor networks
Prusty et al. Performance comparison of supervised machine learning algorithms for multiclass transient classification in a nuclear power plant
Chowdhury et al. Cadence: A practical time-series partitioning algorithm for unlabeled iot sensor streams
Sahinoglu et al. Discovering Granger causality with convolutional neural networks