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    • 1. 发明申请
    • ONE-PASS STATISTICAL COMPUTATIONS
    • 一次统计计算
    • US20130253888A1
    • 2013-09-26
    • US13427626
    • 2012-03-22
    • Hung-Chih YangXiong ZhangDanny B. Lange
    • Hung-Chih YangXiong ZhangDanny B. Lange
    • G06F17/10
    • G06F17/18
    • Some embodiments of the invention employ algorithms enabling the calculation of one or more statistical moments in a single pass of a dataset. For example, some embodiments may apply algorithms for calculating statistical moments to a dataset using a map-reduce framework, whereby an input dataset is partitioned into multiple shards, a separate map process is used to apply an algorithm enabling calculation of one or more statistical moments in a single scan to each shard, and one or more reduce processes consolidate the results generated by the map processes to calculate the one or more statistical moments across the entire dataset. In other embodiments of the invention, a map-reduce framework may be employed to apply algorithms enabling calculation of a covariance between data elements expressed in a dataset, instead of or in addition to one or more statistical moments.
    • 本发明的一些实施例使用能够计算数据集的单次通过中的一个或多个统计矩阵的算法。 例如,一些实施例可以应用用于使用map-reduce框架来计算统计矩阵到数据集的算法,由此将输入数据集划分成多个分片,使用单独的映射过程来应用能够计算一个或多个统计时刻的算法 对每个分片进行单次扫描,并且一个或多个减少过程合并由映射过程生成的结果,以计算整个数据集中的一个或多个统计矩。 在本发明的其他实施例中,可以采用映射减少框架来应用能够计算在数据集中表示的数据元素之间的协方差的算法,而不是一个或多个统计矩阵,或者除了一个或多个统计矩。
    • 2. 发明申请
    • MULTI-CENTER CANOPY CLUSTERING
    • 多中心聚集
    • US20130246429A1
    • 2013-09-19
    • US13423286
    • 2012-03-19
    • Xiong ZhangDanny LangeHung-Chih Yang
    • Xiong ZhangDanny LangeHung-Chih Yang
    • G06F17/30
    • G06F17/30598G06F17/30011
    • A canopy clustering process merges at least one set of multiple single-center canopies together into a merged multi-center canopy. Multi-center canopies, as well as the single-center canopies, can then be used to partition data objects in a dataset. The multi-center canopies allow a canopy assignment condition constraint to be relaxed without risk of leaving any data objects in a dataset outside of all canopies. Approximate distance calculations can be used as similarity metrics to define and merge canopies and to assign data objects to canopies. In one implementation, a distance between a data object and a canopy is represented as the minimum of the distances between the data object and each center of a canopy (whether merged or unmerged), and the distance between two canopies is represented as the minimum of the distances for each pairing of the center(s) in one canopy and the center(s) in the other canopy.
    • 冠层聚类过程将至少一组多个单中心檐篷合并成合并的多中心冠层。 多中心檐篷以及单中心檐篷可用于对数据集中的数据对象进行分区。 多中心檐篷允许放宽冠层分配条件约束,而不会在所有檐篷之外的数据集中留下任何数据对象的风险。 近似距离计算可以用作相似性度量来定义和合并檐篷,并将数据对象分配给檐篷。 在一个实现中,数据对象和冠层之间的距离被表示为数据对象和冠层的每个中心之间的距离的最小值(无论是合并还是未合并),并且两个檐篷之间的距离被表示为 一个冠层中心的每个配对的距离和另一个冠层中的一个或多个中心。
    • 6. 发明授权
    • Identifying influential users of a social networking service
    • 识别社交网络服务的有影响力的用户
    • US09218630B2
    • 2015-12-22
    • US13427584
    • 2012-03-22
    • Hung-Chih YangDanny B. LangeXiong Zhang
    • Hung-Chih YangDanny B. LangeXiong Zhang
    • G06Q50/00G06Q10/10G06Q30/02G09B29/00
    • G06Q30/0201G06Q10/101G06Q30/0251G06Q30/0269G06Q30/0271G06Q50/01G09B29/00H04L51/32H04L51/36H04L67/306
    • Techniques for identifying influential users of a social networking service are provided. Influential users may be identified via an algorithm in which an influence score is assigned to each user based at least in part on other members of the community users having taken an affirmative step with respect to the user's communications. Iterative processing may be performed, with each user's influence score being determined by contributions from other users, and each contribution being determined by the contributor's influence score as of a prior iteration. A map-reduce framework may be employed, with data representing the community being partitioned into a plurality of discrete shards, a map process corresponding to each shard calculating an influence score for users represented in the shard, and reduce processes ranking users according to influence score across all shards.
    • 提供了用于识别社交网络服务的有影响力用户的技术。 可以通过至少部分地基于对用户的通信采取肯定步骤的社区用户的其他成员的算法来识别影响用户,其中影响分数被分配给每个用户。 可以执行迭代处理,每个用户的影响分数由来自其他用户的贡献确定,并且每个贡献由先前迭代中的贡献者的影响分数确定。 可以采用地图缩减框架,其中表示社区的数据被划分成多个离散碎片,对应于每个碎片的映射处理计算分片中表示的用户的影响分数,并且减少根据影响分数对用户进行排名的过程 跨越所有碎片
    • 7. 发明授权
    • Multi-center canopy clustering
    • 多中心冠层聚类
    • US08886649B2
    • 2014-11-11
    • US13423286
    • 2012-03-19
    • Xiong ZhangDanny LangeHung-Chih Yang
    • Xiong ZhangDanny LangeHung-Chih Yang
    • G06F7/00G06F17/30
    • G06F17/30598G06F17/30011
    • A canopy clustering process merges at least one set of multiple single-center canopies together into a merged multi-center canopy. Multi-center canopies, as well as the single-center canopies, can then be used to partition data objects in a dataset. The multi-center canopies allow a canopy assignment condition constraint to be relaxed without risk of leaving any data objects in a dataset outside of all canopies. Approximate distance calculations can be used as similarity metrics to define and merge canopies and to assign data objects to canopies. In one implementation, a distance between a data object and a canopy is represented as the minimum of the distances between the data object and each center of a canopy (whether merged or unmerged), and the distance between two canopies is represented as the minimum of the distances for each pairing of the center(s) in one canopy and the center(s) in the other canopy.
    • 冠层聚类过程将至少一组多个单中心檐篷合并成合并的多中心冠层。 多中心檐篷以及单中心檐篷可用于对数据集中的数据对象进行分区。 多中心檐篷允许放宽冠层分配条件约束,而不会在所有檐篷之外的数据集中留下任何数据对象的风险。 近似距离计算可以用作相似性度量来定义和合并檐篷,并将数据对象分配给檐篷。 在一个实现中,数据对象和冠层之间的距离被表示为数据对象和冠层的每个中心之间的距离的最小值(无论是合并还是未合并),并且两个檐篷之间的距离被表示为 一个冠层中心的每个配对的距离和另一个冠层中的一个或多个中心。