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    DATA ANALYSIS SYSTEM AND METHOD
    44.
    发明申请
    DATA ANALYSIS SYSTEM AND METHOD 审中-公开

    公开(公告)号：US20190108262A1

    公开(公告)日：2019-04-11

    申请号：US16028886

    申请日：2018-07-06

    Applicant: Palantir Technologies Inc.

    Inventor： Myles Scolnick ,  Jack Grossman ,  Jim Inoue

    IPC: G06F17/30

    Abstract: This disclosure relates to a system and method for data analysis. According to a first aspect, there is described a method, the method being performed using one or more processors, comprising: receiving one or more user inputs indicative of one or more relationships between data in a plurality of datasets; determining, based on the one or more user inputs, at least one object view for visualising the data in the plurality of datasets; generating, based on the one or more user inputs, metadata comprising: an object graph indicative of the one or more relationships between two or more of the plurality of datasets; and information identifying the at least one object view; and in response to a query relating to the plurality of datasets, using the metadata to determine how response data responding to the query should be provided.

    Internal malware data item clustering and analysis
    47.
    发明授权
    Internal malware data item clustering and analysis 有权
    内部恶意软件数据项集群和分析

    公开(公告)号：US09344447B2

    公开(公告)日：2016-05-17

    申请号：US14486991

    申请日：2014-09-15

    Applicant: Palantir Technologies Inc.

    Inventor： David Cohen ,  Jason Ma ,  Bing Jie Fu ,  Ilya Nepomnyashchiy ,  Steven Berler ,  Alex Smaliy ,  Jack Grossman ,  James Thompson ,  Julia Boortz ,  Matthew Sprague ,  Parvathy Menon ,  Michael Kross ,  Michael Harris ,  Adam Borochoff

    IPC: G06Q40/00 ,  H04L29/06 ,  G06F17/30

    CPC classification number: H04L63/1425 ,  G06F17/30598 ,  G06Q40/12 ,  H04L63/1408 ,  H04L63/145

    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analysis (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

    Abstract translation: 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，并以优化的方式向分析者提供自动化分析的结果。 集群数据结构（本文中也称为数据集群）的自动化分析可以包括各种标准或规则的自动应用，以便生成数据集群的紧凑的，人类可读的分析。 可以将数据集群的人可读分析（也称为“摘要”或“结论”）组织成交互式用户界面，以使分析人员能够在与各种数据集群相关联的信息之间快速导航，并有效地评估 这些数据集群在例如欺诈调查的背景下。 本公开的实施例还涉及聚类数据结构的自动评分。

    Data item clustering and analysis
    48.
    发明授权
    Data item clustering and analysis 有权
    数据项聚类分析

    公开(公告)号：US09202249B1

    公开(公告)日：2015-12-01

    申请号：US14473552

    申请日：2014-08-29

    Applicant: Palantir Technologies Inc.

    Inventor： David Cohen ,  Jason Ma ,  Bing Jie Fu ,  Ilya Nepomnyashchiy ,  Steven Berler ,  Alex Smaliy ,  Jack Grossman ,  James Thompson ,  Julia Boortz ,  Matthew Sprague ,  Parvathy Menon ,  Michael Kross ,  Michael Harris ,  Adam Borochoff

    IPC: G06Q40/00

    CPC classification number: H04L63/1425 ,  G06F17/30598 ,  G06Q40/12 ,  H04L63/1408 ,  H04L63/145

    Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyzes (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

    Abstract translation: 本公开的实施例涉及一种数据分析系统，其可以自动生成存储器有效的集群数据结构，自动分析这些集群数据结构，并以优化的方式向分析者提供自动化分析的结果。 集群数据结构（本文中也称为数据集群）的自动化分析可以包括各种标准或规则的自动应用，以便生成数据集群的紧凑的，人类可读的分析。 可以将数据集群的可读分析（也称为“摘要”或“结论”）组织成交互式用户界面，以使分析人员能够在与各种数据集群相关联的信息之间快速导航，并有效地评估 这些数据集群在例如欺诈调查的背景下。 本公开的实施例还涉及聚类数据结构的自动评分。

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