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    METHOD AND DEVICE FOR COMPRESSING FLOW DATA
    1.
    发明申请
    METHOD AND DEVICE FOR COMPRESSING FLOW DATA 审中-公开

    公开(公告)号：US20170366197A1

    公开(公告)日：2017-12-21

    申请号：US15697066

    申请日：2017-09-06

    Applicant: HUAWEI TECHNOLOGIES CO.,LTD.

    Inventor： Zhenguo Li ,  Ge Luo ,  Ke Yi ,  Wei Fan ,  Cheng He

    IPC: H03M7/30

    CPC classification number: H03M7/30 ,  H03M7/3064

    Abstract: A method for compressing flow data, including: generating multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

    METHOD AND APPARATUS FOR MINING MAXIMAL REPEATED SEQUENCE
    3.
    发明申请
    METHOD AND APPARATUS FOR MINING MAXIMAL REPEATED SEQUENCE 审中-公开
    挖掘最大重复序列的方法和装置

    公开(公告)号：US20170060998A1

    公开(公告)日：2017-03-02

    申请号：US15349580

    申请日：2016-11-11

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Chen Liang ,  Wei Fan

    IPC: G06F17/30

    CPC classification number: G06F16/3344 ,  G06F16/322

    Abstract: The present invention provide a method and an apparatus for mining a maximal repeated sequence, where a maximal repeated sequence is determined based on pipelines and a suffix tree, thereby implementing incremental mining and improving computation efficiency. The method comprises: acquiring a character; appending the character to each pipeline in a pipeline set, and separately determining whether a sequence in each pipeline appended with the character is the same as a corresponding sequence on a suffix tree; determining a maximal repeated sequence according to a first preset policy and the sequence in the first pipeline when there exists such a first pipeline in the pipeline set that after the character is appended to the first pipeline, a sequence in the first pipeline is different from a corresponding sequence on the suffix tree.

    Abstract translation: 本发明提供一种用于挖掘最大重复序列的方法和装置，其中基于流水线和后缀树确定最大重复序列，从而实现增量挖掘并提高计算效率。 该方法包括：获取字符; 将字符附加到流水线集合中的每个流水线，并单独确定附加有字符的每个流水线中的序列是否与后缀树上的相应序列相同; 当在流水线集合中存在这样的第一流水线时，在字符被附加到第一流水线之后，根据第一预设策略和第一流水线中的序列确定最大重复序列，第一流水线中的序列不同于 后缀树上的相应序列。

    Data category identification method and apparatus based on deep neural network
    5.
    发明授权
    Data category identification method and apparatus based on deep neural network 有权

    公开(公告)号：US10296827B2

    公开(公告)日：2019-05-21

    申请号：US14944294

    申请日：2015-11-18

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Guangjian Tian ,  Cheng He ,  Wei Fan

    IPC: G06N3/08 ,  G06N3/06

    Abstract: A deep neural network to which data category information is added is established locally, to-be-identified data is input to an input layer of the deep neural network generated based on the foregoing data category information, and information of a category to which the to-be-identified data belongs is acquired, where the information of the category is output by an output layer of the deep neural network. A deep neural network is established based on data category information, such that category information of to-be-identified data is conveniently and rapidly obtained using the deep neural network, thereby implementing a category identification function of the deep neural network, and facilitating discovery of an underlying law of the to-be-identified data according to the category information of the to-be-identified data.

    Method, apparatus and computer for identifying state of user of social network
    7.
    发明授权
    Method, apparatus and computer for identifying state of user of social network 有权

    公开(公告)号：US10116759B2

    公开(公告)日：2018-10-30

    申请号：US14948993

    申请日：2015-11-23

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Wei Fan ,  Jianfeng Qian ,  Yadong Mu

    IPC: H04L29/08 ,  G06Q50/00 ,  H04L12/58

    Abstract: A method and an apparatus for identifying a state of a user of a social network. The identification method includes acquiring a user-event similarity of a user regarding a new event; identifying whether the user is a silent user or a non-activated user according to the user-event similarity; and determining whether the silent user or the non-activated user on the social network is finally in an activated state or a non-activated state. In the foregoing manner, a novel user state model of a social network is designed in the present disclosure, the model includes an activated state, a non-activated state and an unstable silent state, and a final state of a user is inferred precisely under full and comprehensive consideration of factors that may affect the state of the user, such that the state of the user can be accurately and precisely monitored.

    METHOD AND DEVICE FOR COMPRESSING FLOW DATA
    9.
    发明申请
    METHOD AND DEVICE FOR COMPRESSING FLOW DATA 有权

    公开(公告)号：US20170250705A1

    公开(公告)日：2017-08-31

    申请号：US15597963

    申请日：2017-05-17

    Applicant: HUAWEI TECHNOLOGIES CO.,LTD.

    Inventor： Zhenguo Li ,  Ge Luo ,  Ke Yi ,  Wei Fan ,  Cheng He

    IPC: H03M7/30

    CPC classification number: H03M7/30 ,  H03M7/3064

    Abstract: A method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

    DATA DISTRIBUTION METHOD, DATA STORAGE METHOD, RELATED APPARATUS, AND SYSTEM
    10.
    发明申请
    DATA DISTRIBUTION METHOD, DATA STORAGE METHOD, RELATED APPARATUS, AND SYSTEM 审中-公开
    数据分配方法，数据存储方法，相关设备和系统

    公开(公告)号：US20160357440A1

    公开(公告)日：2016-12-08

    申请号：US15171794

    申请日：2016-06-02

    Applicant: HUAWEI TECHNOLOGIES CO.,LTD.

    Inventor： Daohui Wang ,  Feng Zhang ,  Wei Fan ,  Zhile Zhang ,  Yongqiang Zeng

    IPC: G06F3/06

    CPC classification number: G06F3/064 ,  G06F3/0604 ,  G06F3/0611 ,  G06F3/0619 ,  G06F3/0631 ,  G06F3/0644 ,  G06F3/067

    Abstract: A data distribution method for improving performance of a distributed storage system includes: receiving, by a data distribution apparatus, a storage instruction of a user, dividing to-be-stored data that the storage instruction instructs to store, into P data segments, determining a storage node group corresponding to each data segment, and finally distributing the data segment to a primary node in the corresponding storage node group.

    Abstract translation: 一种用于提高分布式存储系统的性能的数据分配方法，包括：由数据分配装置将用户的存储指令，存储指令指示存储的存储数据分割成P个数据段，确定 对应于每个数据段的存储节点组，并且最后将数据段分配到相应存储节点组中的主节点。

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