Manage analytics contexts through a series of analytics interactions via a graphical user interface

    公开(公告)号:US10255084B2

    公开(公告)日:2019-04-09

    申请号:US15184478

    申请日:2016-06-16

    Abstract: The present disclosure relates to an interactive system that manages analytics contexts through a series of analytics interactions. The disclosed interactive system receives a selection of an analytics interaction from a user during an interactive analytics session. Then, the system generates a series of analytics interactions by the user during the interactive analytics session. Each analytics interaction represents an analytics context that comprises an analytics interaction, a result, and a reference analytics context. Moreover, the system manages a plurality of analytics contexts by selecting the reference analytics context from previous analytics interactions, or by navigating to a different analytics context, or by deactivating a user-selected analytics context, and presents to the user the series of analytics interactions with the result corresponding to both the selection of the analytics interaction and the reference analytics context. Each analytics interaction in the series of analytics interactions is selectable by the user.

    Real-time alerts and transmission of selected signal samples under a dynamic capacity limitation

    公开(公告)号:US11061395B2

    公开(公告)日:2021-07-13

    申请号:US16071959

    申请日:2016-02-04

    Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

    Identifying groups
    6.
    发明授权

    公开(公告)号:US10534800B2

    公开(公告)日:2020-01-14

    申请号:US15564573

    申请日:2015-04-30

    Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating a group of most frequent elements in a dataset, calculating features of each of the most frequent elements in the dataset, applying a trained model to the features of each of the most frequent elements, and generating a list of predicted relevant elements from the list of most frequent elements. The method further comprises determining at least one element-chain group for each predicted relevant element and a group score for the element-chain-group, ordering a plurality of element-chain groups for the dataset based on the group score for each of the element-chain groups, and identifying a predetermined number of element-chain groups to be outputted to a user.

    TERM CHAIN CLUSTERING
    7.
    发明申请
    TERM CHAIN CLUSTERING 审中-公开
    长链聚集

    公开(公告)号:US20170053024A1

    公开(公告)日:2017-02-23

    申请号:US15306803

    申请日:2014-04-28

    CPC classification number: G06F16/35 G06F16/3344 G06N20/00

    Abstract: According to an example, term chain clustering may include receiving a set of training cases from a known category, and receiving a set of unlabeled cases that are to be analyzed with respect to the known category. A plurality of terms of the set of training cases from the known category, and the set of unlabeled cases that are to be analyzed, may be analyzed using a term scoring function to generate a score for each of the plurality of terms. A highest scoring term may be selected from the analyzed terms based on the score for each of the plurality of terms. A selected set that includes cases from the set of unlabeled cases that include the highest scoring term may be generated.

    Abstract translation: 根据一个示例,术语链聚类可以包括从已知类别接收一组训练情况,以及接收关于已知类别进行分析的一组未标记的病例。 可以使用术语评分函数来分析来自已知类别的一组训练情况的多个术语,以及将要分析的一组未标记的病例,以生成多个术语中的每一个的得分。 可以基于多个术语中的每一个的分数从所分析的术语中选择最高得分项。 可以生成包括来自包括最高得分项的未标记案例集的案例的选定集合。

    Real-time alerts and transmission of selected signal samples under a dynamic capacity limitation

    公开(公告)号:US11899444B2

    公开(公告)日:2024-02-13

    申请号:US17348380

    申请日:2021-06-15

    CPC classification number: G05B23/0297 G06N3/044 H04L63/1425

    Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

    REAL-TIME ALERTS AND TRANSMISSION OF SELECTED SIGNAL SAMPLES UNDER A DYNAMIC CAPACITY LIMITATION

    公开(公告)号:US20190033851A1

    公开(公告)日:2019-01-31

    申请号:US16071959

    申请日:2016-02-04

    Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

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