ANOMALY DETECTION USING HASH SIGNATURE GENERATION FOR MODEL-BASED SCORING

    公开(公告)号:US20240112071A1

    公开(公告)日:2024-04-04

    申请号:US17937254

    申请日:2022-09-30

    IPC分类号: G06N20/00

    CPC分类号: G06N20/00

    摘要: Described systems and techniques provide fast, efficient, and cost-effective techniques for detecting anomalous behaviors of monitored objects. Multiple hashing algorithms, each providing multiple hash bins, may be used to generate a unique hash signature for each of the monitored objects. Metric values characterizing the behavior of the monitored objects may be aggregated within individual ones of the multiple hash bins of each of the multiple hashing algorithms. Then, one or more machine learning models may be trained using the unique hash signatures and their included, aggregated metric values. During subsequent scoring using the trained machine learning model(s), each of the aggregated metric values of each of the hash bins may be scored, and a single or small subset of anomalous objects may be identified.

    SEMANTIC CLASSIFICATION FOR DATA MANAGEMENT
    6.
    发明公开

    公开(公告)号:US20240111736A1

    公开(公告)日:2024-04-04

    申请号:US17937261

    申请日:2022-09-30

    摘要: Described systems and techniques enable fast and accurate semantic classification of data. Such semantic classification may be performed efficiently, e.g., in a manner that uses a minimal required set of resources to perform a given semantic classification. Moreover, described techniques dynamically improve over time, so that even when more resource-intensive operations are initially required to semantically classify data in a first iteration, similar data will be recognized more quickly and using fewer resources in later iterations.

    RUNTIME PREDICTION FOR JOB MANAGEMENT
    7.
    发明公开

    公开(公告)号:US20230418657A1

    公开(公告)日:2023-12-28

    申请号:US17809486

    申请日:2022-06-28

    发明人: Nikolai OZEROV

    IPC分类号: G06F9/48 G06N5/02

    CPC分类号: G06F9/4818 G06N5/02

    摘要: Described techniques provide optimized job management with accurate runtime predictions for individual job instances. By classifying jobs with respect to combinations of multiple prediction algorithms and multiple job properties, including classifying different job instances of a single job, the described techniques enable use of fast, simple prediction techniques while still providing accurate predictions.

    SYSTEMS AND METHODS FOR ISOLATING TRANSACTIONS TO A PSEUDO-WAIT-FOR-INPUT REGION

    公开(公告)号:US20230305941A1

    公开(公告)日:2023-09-28

    申请号:US17656557

    申请日:2022-03-25

    IPC分类号: G06F11/34 G06F16/28

    CPC分类号: G06F11/3495 G06F16/285

    摘要: The system and techniques described herein include receiving a transaction report from a log dataset, where the transaction report includes a class identifier (ID) for each of the transactions, a transaction execution count, and a total transaction response time. A first listing of the transactions is generated. An average number of unique transactions assigned to all of the classes and an average transaction execution count are calculated. A second listing of the transactions is generated that includes all of the transactions for the class IDs where the count of unique transactions assigned to the class is greater than the average number of unique transactions assigned to all of the classes. For each class ID and transaction name, the second listing is updated to identify one or more of the transactions as candidate transactions for running in a P-WFI region when the conditions are met.