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    FACILITATING INTERPRETATION OF HIGH-DIMENSIONAL DATA CLUSTERS
    2.
    发明申请
    FACILITATING INTERPRETATION OF HIGH-DIMENSIONAL DATA CLUSTERS 有权
    促进高维数据集的解读

    公开(公告)号：US20170046597A1

    公开(公告)日：2017-02-16

    申请号：US15307026

    申请日：2014-04-30

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Ming C. Hao ,  Wei-Nchih Lee ,  Alexander Jaeger ,  Nelson L. Chang ,  Daniel Keim

    IPC: G06K9/62

    CPC classification number: G06K9/6224 ,  G06K9/00 ,  G06K9/6215 ,  G06K9/6218 ,  G06K9/6235 ,  G06K2009/6236

    Abstract: In an example, high-dimensional data is projected to a multi-dimensional space to differentiate clusters of the high-dimensional data. A user selection of at least two of the clusters may be received and a plurality of dissimilar dimensions may be extracted from the at least two clusters. In addition, a user selected of a dissimilar dimension from the plurality of extracted dissimilar dimensions may be received. In response to receipt of the user selection of the dissimilar dimension from the plurality of dissimilar dimensions, a plurality of correlated dimensions to the dissimilar dimension may be determined. In addition, the plurality of dissimilar dimensions and the plurality of correlated dimensions may be displayed.

    Abstract translation: 在一个示例中，高维数据被投影到多维空间以区分高维数据的簇。 可以接收至少两个群集的用户选择，并且可以从至少两个群集中提取多个不同维度。 此外，可以接收从多个提取的不同维度中选择不同维度的用户。 响应于从多个不同维度接收到不同维度的用户选择，可以确定与不相似维度的多个相关维度。 此外，可以显示多个不同维度和多个相关维度。

    DISTRIBUTED ANOMALY MANAGEMENT
    4.
    发明申请
    DISTRIBUTED ANOMALY MANAGEMENT 审中-公开

    公开(公告)号：US20170318037A1

    公开(公告)日：2017-11-02

    申请号：US15142687

    申请日：2016-04-29

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Jerome Rolia ,  Martin Arlitt ,  Alberto Cueto ,  Rodrigo Novelo ,  Wei-Nchih Lee ,  Gowtham Bellala

    IPC: H04L29/06

    CPC classification number: H04L63/1416 ,  G06F21/55 ,  G06F21/554 ,  H04L63/1425

    Abstract: Examples relate to distributed anomaly management. In one example, a computing device may: receive real-time anomaly data for a first set of client devices, wherein the received anomaly data includes: anomalous network behavior data received from a network intrusion detection system (NICKS) monitoring network traffic behavior, anomalous host event data received from a host intrusion detection system (HIDS) monitoring host events originating from client devices in the first set, and anomalous process activity data received from a trace intrusion detection system (TIDS) monitoring process activity performed by client devices in the first set; for each client device in the first set of client devices for which anomaly data is received, associate the received anomaly data with the client device; and determine, for a particular client device, a measure of risk, wherein the measure of risk is dynamically adjusted based on the received real-time anomaly data.

    SIMILARITY IN A STRUCTURED DATASET
    5.
    发明申请
    SIMILARITY IN A STRUCTURED DATASET 审中-公开

    公开(公告)号：US20170177704A1

    公开(公告)日：2017-06-22

    申请号：US15325630

    申请日：2014-07-29

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Wei-Nchih Lee ,  Jerome Rolia

    IPC: G06F17/30

    CPC classification number: G06F16/285 ,  G06F16/243 ,  G06F16/258 ,  G06F16/36

    Abstract: Detecting similarity in a structured dataset is disclosed. One example is a system including a converter, and an evaluator. A structured dataset is received via a processing system, the dataset including a plurality of objects, each object of the plurality of objects associated with a category, and each category associated with an object label. The converter converts, for each object of the plurality of objects, the object label into a semantic term, The evaluator determines, via the processing system, a term similarity for a pair of object labels in a given category, the term similarity indicative of a correlation between the respective semantic terms in the given category.

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