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    Invariants Modeling and Detection for Heterogeneous Logs
    45.
    发明申请
    Invariants Modeling and Detection for Heterogeneous Logs 审中-公开

    公开(公告)号：US20170277997A1

    公开(公告)日：2017-09-28

    申请号：US15430024

    申请日：2017-02-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： Bo Zong ,  Jianwu Xu ,  Guofei Jiang

    IPC: G06N5/02 ,  G06N99/00

    CPC classification number: G06F16/2477 ,  G06F11/3072 ,  G06F16/35 ,  G06N5/045

    Abstract: A method is provided that is performed in a network having nodes that generate heterogeneous logs including performance logs and text logs. The method includes performing, during a heterogeneous log training stage, (i) a log-to-time sequence conversion process for transforming clustered ones of training logs, from among the heterogeneous logs, into a set of time sequences that are each formed as a plurality of data pairs of a first configuration and a second configuration based on cluster type, (ii) a time series generation process for synchronizing particular ones of the time sequences in the set based on a set of criteria to output a set of fused time series, and (iii) an invariant model generation process for building invariant models for each time series data pair in the set of fused time series. The method includes controlling an anomaly-initiating one of the plurality of nodes based on the invariant models.

    Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
    49.
    发明申请
    Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations 审中-公开

    公开(公告)号：US20170228277A1

    公开(公告)日：2017-08-10

    申请号：US15420949

    申请日：2017-01-31

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wei Cheng ,  Kai Zhang ,  Haifeng Chen ,  Guofei Jiang

    IPC: G06F11/07 ,  G06N99/00

    CPC classification number: G06F11/079 ,  G06F11/0709 ,  G06F11/0751 ,  G06F2201/805 ,  G06F2201/81 ,  G06N99/005

    Abstract: A method is provided for root cause anomaly detection in an invariant network having a plurality of nodes that generate time series data. The method includes modeling anomaly propagation in the network. The method includes reconstructing broken invariant links in an invariant graph based on causal anomaly ranking vectors. Each broken invariant link involves a respective node pair formed from the plurality of nodes such that one of the nodes in the respective node pair has an anomaly. Each causal anomaly ranking vector is for indicating a respective node anomaly status for a given one of the plurality of nodes when paired. The method includes calculating a sparse penalty of the casual anomaly ranking vectors to obtain a set of time-dependent anomaly rankings. The method includes performing temporal smoothing of the set of rankings, and controlling an anomaly-initiating one of the plurality of nodes based on the set of rankings.

    Extracting interpretable features for classification of multivariate time series from physical systems
    50.
    发明授权
    Extracting interpretable features for classification of multivariate time series from physical systems 有权

    公开(公告)号：US09652716B2

    公开(公告)日：2017-05-16

    申请号：US14527413

    申请日：2014-10-29

    Applicant: NEC Laboratories America, Inc.

    Inventor： Abhishek Sharma ,  Haifeng Chen ,  Guofei Jiang ,  Om Prasad Patri

    IPC: G06F15/00 ,  G06F15/18 ,  G06N5/04 ,  G06N5/00

    CPC classification number: G06N5/043 ,  G06N5/003

    Abstract: A method and system are provided. The method includes extracting shapelets from each of a plurality of time series dimensions of multi-dimensional time series data. The method further includes building a plurality of decision-tree classifiers, one for each time series dimension, responsive to the shapelets extracted therefrom. The method also includes generating a pairwise similarity matrix between respective different ones of the plurality of time series dimensions using the shapelets as intermediaries for determining similarity. The method additionally includes applying a feature selection technique to the matrix to determine respective feature weights for each of shapelet features of the shapelets and respective classifier weights for each of the decision-tree classifiers that uses the shapelet features. The method further includes combining decisions issued from the decision-tree classifiers to generate a final verdict of classification for a time series dimension responsive to the respective feature weights and the respective classifier weights.

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