Joint semantic and format similarity for large scale log retrieval

    公开(公告)号:US10929218B2

    公开(公告)日:2021-02-23

    申请号:US16400402

    申请日:2019-05-01

    Abstract: A method for diagnosing computer system faults using log retrieval based on joint semantic and syntactic similarities includes receiving a set of query logs, defining joint semantic and syntactic similarities between the set of query logs and respective ones of multiple sets of historical logs based on semantic content and syntactic information obtained for the set of query logs and the multiple sets of historical logs, the multiple sets of historical logs being associated with historical computer system fault diagnoses, retrieving a set of historical logs from the multiple sets of historical logs to obtain a retrieved set of historical logs for computer system fault comparison based on a similarity measure corresponding to each of the multiple sets of historical logs derived from the joint semantic and syntactic similarities, and transmitting the retrieved set of historical logs to one or more computing devices to perform the computer system fault comparison.

    Content aware heterogeneous log pattern comparative analysis engine

    公开(公告)号:US10706229B2

    公开(公告)日:2020-07-07

    申请号:US16145580

    申请日:2018-09-28

    Abstract: A computer-implemented method, system, and computer program product are provided for content aware heterogeneous log pattern comparative analysis. The method includes receiving, by a processor-device, a plurality of heterogeneous logs. The method also includes extracting, by the processor-device, a plurality of log syntactic patterns from the plurality of heterogenous logs. The method additionally includes generating, by the processor-device, latent representation vectors for each of the plurality of log syntactic patterns. The method further includes predicting, by the processor-device, an anomaly from the clustered latent representation vectors. The method also includes controlling an operation of a processor-based machine to react in accordance with the anomaly.

    COMPUTER LOG RETRIEVAL BASED ON MULTIVARIATE LOG TIME SERIES

    公开(公告)号:US20190354524A1

    公开(公告)日:2019-11-21

    申请号:US16400348

    申请日:2019-05-01

    Abstract: Systems and methods for computer log retrieval are provided. A system can receive a set of query logs, and transform the set of query logs into a query log multi-variate time series. The system accesses log multivariate time series of historical logs, and computes and ranks a similarity distance between the query log multivariate time series and each of the log multivariate time series of the historical logs. The system also retrieves a highest ranked set of historical logs as a most similar set of logs compared to the set of query logs.

    Multibyte heterogeneous log preprocessing

    公开(公告)号:US10474642B2

    公开(公告)日:2019-11-12

    申请号:US15659131

    申请日:2017-07-25

    Abstract: Methods and systems for log management include pre-processing heterogeneous logs and performing a log management action on the pre-processed plurality of heterogeneous logs. Pre-processing the logs includes performing a fixed tokenization of the heterogeneous logs based on a predefined set of symbols, performing a flexible tokenization of the heterogeneous logs based on a user-defined set of rules, converting timestamps in the heterogeneous logs to a single target timestamp format, and performing structural log tokenization of the heterogeneous logs based on user-defined structural information.

    ADAPTIVE CONTINUOUS LOG MODEL LEARNING
    55.
    发明申请

    公开(公告)号:US20190340540A1

    公开(公告)日:2019-11-07

    申请号:US16400426

    申请日:2019-05-01

    Inventor: Jianwu Xu Hui Zhang

    Abstract: Systems and methods for adaptive and continuous log model learning can include updating a core model to generate an updated core model, each being a syntactic model and being additive in nature, based on a heterogeneous training log file and updating a peripheral model, that represents a relationship between core models, using a set of existing auxiliary files, that define can define relationship between existing models, and the updated core model to generate an updated peripheral model based on the heterogeneous training log file. Additionally, they can include detecting, with the updated core model and the updated peripheral model, an anomaly within a set of testing logs indicative of information technology system operation to take remedial action on the information technology system based on a most recent model update.

    EFFICIENT EVENT SEARCHING
    56.
    发明申请

    公开(公告)号:US20190171644A1

    公开(公告)日:2019-06-06

    申请号:US16207644

    申请日:2018-12-03

    Abstract: Methods and systems for event detection and correction include determining a log pattern for a received event. The log pattern is translated to an event search query. The event search query is weighted according to discriminative dimensions using term-frequency inverse-document-frequency. The event search query is matched to one or more known events. A corrective action is automatically performed based on a solution associated with the one or more known events.

    Automated anomaly detection service on heterogeneous log streams

    公开(公告)号:US09928155B2

    公开(公告)日:2018-03-27

    申请号:US15352546

    申请日:2016-11-15

    CPC classification number: G06F11/3612 G06F11/0706 G06F11/0766 G06F11/3636

    Abstract: Systems and methods are disclosed for handling log data from one or more applications, sensors or instruments by receiving heterogeneous logs from arbitrary/unknown systems or applications; generating regular expression patterns from the heterogeneous log sources using machine learning and extracting a log pattern therefrom; generating models and profiles from training logs based on different conditions and updating a global model database storing all models generated over time; tokenizing raw log messages from one or more applications, sensors or instruments running a production system; transforming incoming tokenized streams are into data-objects for anomaly detection and forwarding of log messages to various anomaly detectors; and generating an anomaly alert from the one or more applications, sensors or instruments running a production system.

    Invariants Modeling and Detection for Heterogeneous Logs

    公开(公告)号:US20170277997A1

    公开(公告)日:2017-09-28

    申请号:US15430024

    申请日:2017-02-10

    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.

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