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    Context-sensitive views
    22.
    发明授权
    Context-sensitive views 有权

    公开(公告)号：US10545655B2

    公开(公告)日：2020-01-28

    申请号：US15878914

    申请日：2018-01-24

    Applicant: Palantir Technologies Inc.

    Inventor： Joshua Goldenberg ,  Brian Ngo ,  Bill Dwyer ,  Parvathy Menon ,  Gregory Martin ,  Zach Bush ,  Allen Chang ,  Mike Boland

    IPC: G06F3/048 ,  G06F3/0485 ,  G06F16/00 ,  G06F16/904 ,  G06F3/0481 ,  G06F3/0484 ,  G06F3/0482

    Abstract: A context-sensitive viewing system is disclosed in which various data visualizations, also referred to a contextual views, of a common set of data may be viewed by a user on an electronic device. Data in the system may comprise data objects and associated properties and/or metadata, and may be stored in one or more electronic data stores. As a user of the system views and manipulates a first contextual view of a set of data objects, one or more other contextual views of the same set of data objects may be updated accordingly. Updates to the secondary contextual views may, in various embodiments, happen real-time. Further, the secondary contextual views may be visible to the user simultaneously with the primary contextual view. A user may switch from one view to another, and may manipulate data in any view, resulting in updates in the other views.

    NETWORK INTRUSION DATA ITEM CLUSTERING AND ANALYSIS
    25.
    发明申请
    NETWORK INTRUSION DATA ITEM CLUSTERING AND ANALYSIS 有权
    网络入侵数据项集合与分析

    公开(公告)号：US20160366164A1

    公开(公告)日：2016-12-15

    申请号：US14487021

    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: H04L29/06

    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 analyses (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。

    GENERATING DATA CLUSTERS
    26.
    发明申请
    GENERATING DATA CLUSTERS 有权
    生成数据集

    公开(公告)号：US20140310282A1

    公开(公告)日：2014-10-16

    申请号：US14264445

    申请日：2014-04-29

    Applicant: Palantir Technologies, Inc.

    Inventor： Matthew Sprague ,  Michael Kross ,  Adam Borochoff ,  Parvathy Menon ,  Michael Harris

    IPC: G06Q30/00

    CPC classification number: G06F17/3053 ,  G06F17/30345 ,  G06F17/30412 ,  G06F17/30539 ,  G06F17/30572 ,  G06F17/30598 ,  G06F17/30601 ,  G06F17/30604 ,  G06F17/30699 ,  G06F17/30705 ,  G06F17/3071 ,  G06F17/30867 ,  G06Q10/10 ,  G06Q20/4016 ,  G06Q30/0185 ,  G06Q40/00 ,  G06Q40/02 ,  G06Q40/025 ,  G06Q40/10 ,  G06Q40/123

    Abstract: Techniques are disclosed for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

    Abstract translation: 公开了用于优先考虑多个聚类的技术。 优先化集群通常可以包括识别用于对多个集群进行优先级排序的评分策略。 每个集群都是从种子生成的，并存储使用种子检索的数据集合。 对于每个集群，根据评分策略评估集群存储的数据集合的元素，并根据评估将分数分配给集群。 可以根据分配给多个聚类的各个分数对聚类进行排名。 由每个集群存储的数据的收集可以包括通过得分策略评估的财务数据以获得欺诈的风险。 分配给每个集群的分数可能对应于处于风险中的金额。

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