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    • 5. 发明授权
    • Learning-based data decontextualization
    • 基于学习的数据解密
    • US09342796B1
    • 2016-05-17
    • US14028396
    • 2013-09-16
    • AMAZON TECHNOLOGIES, INC.
    • Jon Arron McClintockGeorge Nikolaos StathakopoulosDominique Imjya Brezinski
    • G06N99/00
    • G06N99/005
    • Techniques are described for employing a crowdsourcing framework to analyze data related to the performance or operations of computing systems, or to analyze other types of data. A question is analyzed to determine data that is relevant to the question. The relevant data may be decontextualized to remove or alter contextual information included in the data, such as sensitive, personal, or business-related data. The question and the decontextualized data may then be presented to workers in a crowdsourcing framework, and the workers may determine an answer to the question based on an analysis or an examination of the decontextualized data. The answers may be combined, correlated, or otherwise processed to determine a processed answer to the question. Machine learning techniques are employed to adjust and refine the decontextualization.
    • 描述了使用众包框架来分析与计算系统的性能或操作相关的数据或分析其他类型的数据的技术。 分析一个问题来确定与问题相关的数据。 相关数据可以被解构化以去除或改变包括在数据中的上下文信息,诸如敏感的,个人的或与业务有关的数据。 然后可以在众包框架中将问题和解构图数据提供给工人,并且工作人员可以基于分析或检验解构数据来确定问题的答案。 答案可以组合,相关或以其他方式处理,以确定问题的处理答案。 机器学习技术被用于调整和完善解构文化。