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    IDENTIFYING GROUPS
    11.
    发明申请
    IDENTIFYING GROUPS 审中-公开

    公开(公告)号：US20180081961A1

    公开(公告)日：2018-03-22

    申请号：US15564573

    申请日：2015-04-30

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Hila Nachlieli ,  George Forman ,  Renato Keshet

    IPC: G06F17/30 ,  G06N99/00 ,  G06N5/04 ,  G06F5/01

    CPC classification number: G06F16/285 ,  G06F5/01 ,  G06K9/6218 ,  G06N5/04 ,  G06N99/00

    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.

    RESIDUAL DATA IDENTIFICATION
    12.
    发明申请
    RESIDUAL DATA IDENTIFICATION 审中-公开
    残留数据标识

    公开(公告)号：US20160267168A1

    公开(公告)日：2016-09-15

    申请号：US15033181

    申请日：2013-12-19

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： George H. Forman ,  Renato Keshet

    IPC: G06F17/30 ,  G06N99/00

    CPC classification number: G06F16/285 ,  G06F16/24578 ,  G06F16/90 ,  G06N20/00

    Abstract: A technique for residual data identification can include receiving a plurality of data instances in a multi-class training data set that are d as belonging to recognized categories, receiving a plurality of data instances a first unlabeled data set, and receiving a plurality of data instances in a second unlabeled data set A technique for residual data identification can include labeling the plurality of data instances in the multi-class training data set as negative data instances. A technique for residual data identification can include labeling the plurality of data instances in the first unlabeled data set as positive data instances. A technique for residual data identification can include training a classifier with the labeled negative data instances and the labeled positive data instances. A technique for residual data identification can include applying the classifier to identify residual data instances in the second unlabeled data set.

    Abstract translation: 用于残差数据识别的技术可以包括：将多个训练数据集中的多个数据实例接收为d属于所识别的类别，接收多个数据实例第一未标记的数据集，以及接收多个数据实例 在第二未标记数据集中用于残差数据识别的技术可以包括将多类训练数据集中的多个数据实例标记为负数据实例。 用于残差数据识别的技术可以包括将第一未标记数据集中的多个数据实例标记为正数据实例。 用于残差数据识别的技术可以包括用标记的负数据实例和标记的正数据实例来训练分类器。 用于残差数据识别的技术可以包括应用分类器来识别第二未标记数据集中的残留数据实例。

    MULTI-DIMENSIONAL DATA SAMPLES REPRESENTING ANOMALOUS ENTITIES
    15.
    发明申请
    MULTI-DIMENSIONAL DATA SAMPLES REPRESENTING ANOMALOUS ENTITIES 审中-公开

    公开(公告)号：US20180248900A1

    公开(公告)日：2018-08-30

    申请号：US15445477

    申请日：2017-02-28

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Renato Keshet ,  Yaniv Sabo

    IPC: H04L29/06 ,  G06F21/55

    CPC classification number: H04L63/1425 ,  G06F21/552 ,  G06N20/10

    Abstract: In some examples, a plurality of multi-dimensional data samples representing respective behaviors of entities in a computing environment are sorted, where the sorting is based on values of dimensions of each respective multi-dimensional data sample. For a given multi-dimensional data sample, a subset of the plurality of multi-dimensional data samples is selected based on the sorting. An anomaly indication is computed for the given multi-dimensional data sample based on applying a function on the multi-dimensional data samples in the subset. It is determined whether the given multi-dimensional data sample represents an anomalous entity in the computing environment based on the computed anomaly indication.

    MANAGE ANALYTICS CONTEXTS THROUGH A SERIES OF ANALYTICS INTERACTIONS VIA A GRAPHICAL USER INTERFACE
    17.
    发明申请
    MANAGE ANALYTICS CONTEXTS THROUGH A SERIES OF ANALYTICS INTERACTIONS VIA A GRAPHICAL USER INTERFACE 审中-公开

    公开(公告)号：US20170364373A1

    公开(公告)日：2017-12-21

    申请号：US15184478

    申请日：2016-06-16

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Olga Shain ,  Yaniv Sabo ,  Renato Keshet

    IPC: G06F9/44 ,  G06F17/30 ,  G06F3/0481

    CPC classification number: G06F9/453 ,  G06F3/04817 ,  G06F17/3053

    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.

    DETERMINING TERM SCORES BASED ON A MODIFIED INVERSE DOMAIN FREQUENCY
    20.
    发明申请
    DETERMINING TERM SCORES BASED ON A MODIFIED INVERSE DOMAIN FREQUENCY 审中-公开

    公开(公告)号：US20170154107A1

    公开(公告)日：2017-06-01

    申请号：US15325807

    申请日：2014-12-11

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Morad Awad ,  Gil Elgrably ,  Mani Fischer ,  Renato Keshet ,  Mike Krohn ,  Alina Maor ,  Ron Maurer ,  Igor Nor ,  Olga Shain ,  Doron Shaked

    IPC: G06F17/30

    CPC classification number: G06F16/345 ,  G06F16/35 ,  G06F16/36

    Abstract: Determining term scores based on a modified inverse domain frequency is disclosed. One example is a system including a data processing engine, an evaluator, and a data analytics module. The data processing engine identifies a key term associated with a system, and a sub-plurality of a plurality of documents, the sub-plurality of documents associated with the event. The evaluator determines, based on the presence or absence of the key term, a first distribution related to the sub-plurality of documents, and a second distribution related to the plurality of documents, and evaluates, for the key term, a term score based on the first distribution and the second distribution, the term score indicative of a modified inverse domain frequency based on the sub-plurality of documents. The data analytics module includes the key term in a word cloud when the term score for the key term satisfies a threshold.

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