公开(公告)号：US20080177833A1

公开(公告)日：2008-07-24

申请号：US12038386

申请日：2008-02-27

申请人： XIAOHUI GU ,  Zon-Yin Shae ,  Zhen Wen ,  Philip S. Yu

发明人： XIAOHUI GU ,  Zon-Yin Shae ,  Zhen Wen ,  Philip S. Yu

IPC分类号： G06F15/16

CPC分类号： H04L12/1818 ,  H04L45/16 ,  H04L45/48

摘要： A system and computer program product for establishing multi-party VoIP conference audio calls in a distributed, peer-to-peer network where any number of nodes are able to arbitrarily and asynchronously start or stop producing audio output to be mixed into a single composite audio stream that is distributed to all nodes. A single distribution tree is used that has optimal communications characteristics to distribute the composite audio signal to all nodes. An audio mixing tree is established and maintained by adaptively and dynamically adding and merging intermediate mixing nodes operating between user nodes and the root of the single distribution tree. The intermediate mixing nodes and the root of the single distribution tree are all hosted, in an exemplary embodiment, on user nodes that are endpoints of the distribution tree.

摘要翻译： 一种用于在分布式对等网络中建立多方VoIP会议音频呼叫的系统和计算机程序产品，其中任何数量的节点能够任意地和异步地开始或停止产生要混合到单个复合音频中的音频输出 分发给所有节点的流。 使用具有最佳通信特性以将复合音频信号分配给所有节点的单个分发树。 通过自适应地动态地添加和合并在用户节点和单个分发树的根之间运行的中间混合节点来建立和维护音频混合树。 在示例性实施例中，分发树的中间混合节点和根分别在作为分发树的端点的用户节点上托管。

公开(公告)号：US5490248A

公开(公告)日：1996-02-06

申请号：US364052

申请日：1994-12-27

申请人： Asit Dan ,  Kun-lung Wu ,  Philip S. Yu

发明人： Asit Dan ,  Kun-lung Wu ,  Philip S. Yu

IPC分类号： G06F3/06 ,  G06F11/10 ,  G06F12/08 ,  G06F12/12 ,  G11B20/18 ,  G06F11/00

CPC分类号： G06F11/1076 ,  G06F12/122 ,  G11B20/1833 ,  G06F12/0866 ,  G06F2211/1009 ,  G06F2211/1095

摘要： In a digital storage disk array system in which parity blocks are created and stored in order to be able to recover lost data blocks in the event of failure of a disk, high-activity parity groups are created for data blocks having high write activity and low-activity parity groups are created for data blocks not having high write activity. High-activity parity blocks formed from the high-activity data blocks are then stored in a buffer memory of a controller rather than on the disks in order to reduce the number of disk accesses during updating.

摘要翻译： 在其中创建和存储奇偶校验块以便在磁盘发生故障的情况下能够恢复丢失的数据块的数字存储磁盘阵列系统中，为具有高写入活动和低的数据块创建高活动奇偶校验组 为不具有高写入活动的数据块创建活动奇偶校验组。 然后，由高活动性数据块形成的高活动性奇偶校验块存储在控制器的缓冲存储器中，而不是在磁盘上，以便在更新期间减少磁盘访问次数。

公开(公告)号：US5437032A

公开(公告)日：1995-07-25

申请号：US293257

申请日：1994-08-19

申请人： Joel L. Wolf ,  Philip S. Yu ,  John J. E. Turek

发明人： Joel L. Wolf ,  Philip S. Yu ,  John J. E. Turek

IPC分类号： G06F15/16 ,  G06F9/46 ,  G06F9/48 ,  G06F9/50 ,  G06F15/177

CPC分类号： G06F9/4881 ,  G06F9/5033 ,  G06F2209/483 ,  G06F2209/5017

摘要： A task scheduler for use in a multiprocessor, multitasking system in which a plurality of processor complexes, each containing one or more processors, concurrently execute tasks into which jobs such as database queries are divided. A desired level of concurrent task activity, such as the maximum number of tasks that can be executed concurrently without queuing of tasks, is defined for each processor complex. Each job is assigned a weight in accordance with the external priority accorded to the job. For each job there is defined a desired level of concurrent; task activity that is proportional to its share of the total weight assigned to all concurrently executing jobs. The jobs are prioritized for execution of awaiting tasks in accordance with the discrepancy between the desired level of multitasking activity and the actual level of multitasking activity for each job. Awaiting tasks are preferentially scheduled from jobs with the largest discrepancy between the desired and actual levels of concurrent task activity and are preferentially assigned to the processor complexes with the largest discrepancy between the desired and actual levels of concurrent task activity. The scheduler attempts to assign each task to a processor for which the task has an affinity or at least neutrality in terms of relative execution speed.

摘要翻译： 一种在多处理器，多任务系统中使用的任务调度器，其中多个处理器复合体（每个处理器复合体包含一个或多个处理器）并行地执行诸如数据库查询之类的作业被分割的任务。 为每个处理器复杂度定义了并发任务活动的期望级别，例如可并行执行并且不排队任务的任务的最大数量。 每个作业都按照与工作相符的外部优先级分配重量。 对于每个作业，定义了一个期望的并发级别; 任务活动与其分配给所有并行执行作业的总重量的份额成正比。 根据所需级别的多任务活动与每个作业的多任务活动的实际水平之间的差异，这些作业被优先执行等待任务。 等待任务优先从并发任务活动的期望和实际水平之间的最大差异的作业调度，并且优先地分配给期望和实际并发任务活动之间的最大差异的处理器复合体。 调度程序尝试将每个任务分配给处理器，对于该处理器，任务具有相对于执行速度的亲和度或至少中立性。

公开(公告)号：US20120281894A1

公开(公告)日：2012-11-08

申请号：US13491561

申请日：2012-06-07

申请人： Michail Vlachos ,  Philip S. Yu

发明人： Michail Vlachos ,  Philip S. Yu

IPC分类号： G06K9/36

CPC分类号： G06T1/0071 ,  G06T2201/0051 ,  G06T2201/0052 ,  G06T2201/0061 ,  G06T2201/0083

摘要： Systems and methods for embedding metadata such as personal patient information within actual medical data signals obtained from a patient are provided wherein two watermarks, a robust watermark and a fragile watermark are embedded in a given medical data signal. The robust watermark includes a binary coded representation of the metadata that is incorporated into the frequency domain of the medical data signal using discrete Fourier transformations and additive embedding. Error correcting code can also be added to the binary representation of the metadata using Hamming coding. A given robust watermark can be incorporated multiple times in the medical data signal. The fragile watermark is added on top of the modified medical signal containing the robust watermark in the spatial domain of the modified medical signal. The fragile watermark utilizes hash function to generate random sequences that are incorporated through the medical data signal.

摘要翻译： 提供了用于将诸如个人患者信息之类的元数据嵌入到从患者获得的实际医疗数据信号中的系统和方法，其中在给定医疗数据信号中嵌入两个水印，鲁棒水印和脆弱水印。 鲁棒水印包括使用离散傅里叶变换和附加嵌入结合到医疗数据信号的频域中的元数据的二进制编码表示。 错误纠正码也可以使用汉明编码加到元数据的二进制表示中。 给定的鲁棒水印可以被并入多次在医疗数据信号中。 在修改后的医疗信号的空间域中包含鲁棒水印的经修改的医学信号之上添加脆弱水印。 脆弱水印利用散列函数产生通过医疗数据信号并入的随机序列。

公开(公告)号：US08060461B2

公开(公告)日：2011-11-15

申请号：US12372568

申请日：2009-02-17

申请人： Yun Chi ,  Haixun Wang ,  Philip S. Yu

发明人： Yun Chi ,  Haixun Wang ,  Philip S. Yu

IPC分类号： G06F7/00 ,  G06F17/00

CPC分类号： G06K9/6297 ,  H04L43/028

摘要： Load shedding schemes for mining data streams. A scoring function is used to rank the importance of stream elements, and those elements with high importance are investigated. In the context of not knowing the exact feature values of a data stream, the use of a Markov model is proposed herein for predicting the feature distribution of a data stream. Based on the predicted feature distribution, one can make classification decisions to maximize the expected benefits. In addition, there is proposed herein the employment of a quality of decision (QoD) metric to measure the level of uncertainty in decisions and to guide load shedding. A load shedding scheme such as presented herein assigns available resources to multiple data streams to maximize the quality of classification decisions. Furthermore, such a load shedding scheme is able to learn and adapt to changing data characteristics in the data streams.

摘要翻译： 挖掘数据流的加载脱落方案。 使用评分函数对流元素的重要性进行排序，并调查那些具有重要意义的元素。 在不知道数据流的精确特征值的上下文中，本文提出了使用马尔可夫模型来预测数据流的特征分布。 基于预测的特征分布，可以进行分类决定，以最大限度地提高预期效益。 此外，在此提出采用质量决策（QoD）度量来衡量决策中的不确定性水平并指导负荷脱落。 诸如此处呈现的负载脱落方案将可用资源分配给多个数据流以最大化分类决定的质量。 此外，这种负载脱落方案能够学习和适应数据流中不断变化的数据特性。

公开(公告)号：US07933740B2

公开(公告)日：2011-04-26

申请号：US12550571

申请日：2009-08-31

申请人： Vittorio Castelli ,  Michail Vlachos ,  Philip S. Yu

发明人： Vittorio Castelli ,  Michail Vlachos ,  Philip S. Yu

IPC分类号： G06F17/18 ,  H04B15/00

CPC分类号： G06K9/00523 ,  Y10S706/90 ,  Y10S707/99936 ,  Y10S707/99937 ,  Y10S707/99945 ,  Y10S707/99953

摘要： Arrangements and methods for performing structural clustering between different time series. Time series data relating to a plurality of time series is accepted, structural features relating to the time series data are ascertained, and at least one distance between different time series via employing the structural features is determined. The different time series may be partitioned into clusters based on the at least one distance, and/or the k closest matches to a given time series query based on the at least one distance may be returned.

摘要翻译： 在不同时间序列之间进行结构聚类的布置和方法。 接收与多个时间序列相关的时间序列数据，确定与时间序列数据相关的结构特征，并且确定通过采用结构特征的不同时间序列之间的至少一个距离。 可以基于至少一个距离将不同的时间序列划分成簇，并且可以返回基于至少一个距离的/或与给定时间序列查询的k个最接近的匹配。

公开(公告)号：US07904397B2

公开(公告)日：2011-03-08

申请号：US12690502

申请日：2010-01-20

申请人： Wei Fan ,  Haixun Wang ,  Philip S. Yu

发明人： Wei Fan ,  Haixun Wang ,  Philip S. Yu

IPC分类号： G06F15/18 ,  G06N3/00 ,  G06N3/12

CPC分类号： G06N99/005

摘要： A method (and structure) for processing an inductive learning model for a dataset of examples, includes dividing the dataset of examples into a plurality of subsets of data and generating, using a processor on a computer, a learning model using examples of a first subset of data of the plurality of subsets of data. The learning model being generated for the first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of the dataset, the ensemble model thereby providing an evolving estimated learning model for the entirety of the dataset if all the subsets were to be processed. The generating of the learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of the ensemble model.

摘要翻译： 一种用于处理实例的数据集的感应学习模型的方法（和结构），包括将示例的数据集划分成多个数据子集，并使用计算机上的处理器生成使用第一子集的示例的学习模型 的多个数据子集的数据。 为第一子集生成的学习模型包括用于整个数据集的演进聚合学习模型（集合模型）的初始阶段，从而为整个数据集提供演进的估计学习模型，如果所有子集 被处理。 使用来自子集的数据生成学习模型包括计算至少一个参数的值，所述参数提供对所述集合模型的当前阶段的充分性的客观指示。

公开(公告)号：US07739331B2

公开(公告)日：2010-06-15

申请号：US12054207

申请日：2008-03-24

申请人： Xiaohui Gu ,  Philip S. Yu

发明人： Xiaohui Gu ,  Philip S. Yu

IPC分类号： G06F15/16 ,  G06F15/173 ,  G06F7/00

CPC分类号： G06F17/30985 ,  Y10S707/99932

摘要： A computer implemented method, apparatus, and computer usable program code for performing load diffusion to process data stream pairs. A data stream pair is received for correlation. The data stream pair is partitioned into portions to meet correlation constraints for correlating data in the data stream pair to form a partitioned data stream pair. The partitioned data stream pair is sent to a set of nodes for correlation processing to perform the load diffusion.

摘要翻译： 用于执行负载扩散以处理数据流对的计算机实现的方法，装置和计算机可用程序代码。 接收数据流对以进行相关。 将数据流对划分成部分以满足用于使数据流对中的数据相关的相关约束，以形成分区数据流对。 分区数据流对被发送到一组节点进行相关处理以执行负载扩散。

公开(公告)号：US07702620B2

公开(公告)日：2010-04-20

申请号：US11693471

申请日：2007-03-29

申请人： Hao He ,  Philip S. Yu ,  Haixun Wang

发明人： Hao He ,  Philip S. Yu ,  Haixun Wang

IPC分类号： G06F17/30

CPC分类号： G06F17/30625 ,  G06F17/30616 ,  G06F17/30675 ,  Y10S707/99933

摘要： Arrangements and methods for providing for the efficient implementation of ranked keyword searches on graph-structured data. Since it is difficult to directly build indexes for general schemaless graphs, conventional techniques highly rely on graph traversal in running time. The previous lack of more knowledge about graphs also resulted in great difficulties in applying pruning techniques. To address these problems, there is introduced herein a new scoring function while the block is used as an intermediate access level; the result is an opportunity to create sophisticated indexes for keyword search. Also proposed herein is a cost-balanced expansion algorithm to conduct a backward search, which provides a good theoretical guarantee in terms of the search cost.

摘要翻译： 用于提供在图形结构化数据上有效执行排名关键词搜索的安排和方法。 由于难以直接构建一般无法图的索引，常规技术高度依赖于运行时间的图遍历。 以前缺乏对图形的更多了解也导致了应用修剪技术的巨大困难。 为了解决这些问题，这里引入了一个新的评分功能，而块被用作中间访问级别; 结果是为关键字搜索创建复杂索引的机会。 这里还提出了一种用于进行后向搜索的成本平衡的扩展算法，这在搜索成本方面提供了良好的理论保证。

公开(公告)号：US20090281971A1

公开(公告)日：2009-11-12

申请号：US12118405

申请日：2008-05-09

申请人： Charu C. Aggarwal ,  Philip S. Yu

发明人： Charu C. Aggarwal ,  Philip S. Yu

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06K9/6267

摘要： Systems and methods for object classification are provided. An object is identified along with the attributes that describe that object. These attributes are grouped into attribute patterns. Classes to be used in the classification are also identified. For each identified class a sketch table containing a plurality of parallel hash tables is created and trained using known objects with known classifications. For the object to be classified, each attribute pattern is processed using the all of the hash functions for each sketch table. This results in a plurality of values under each sketch table for a single attribute pattern. The lowest value is selected for each sketch table. The distribution of values across all sketch tables is evaluated for each attribute pattern. This produces a discriminatory power for each attribute pattern. Those attribute patterns having a discriminatory power above a given threshold are selected. The selected attribute patterns and associated sketch table values are added. The sketch table with the largest overall sum is identified, and the class associated with that sketch table is assigned to the object to which the attribute patterns belong.

摘要翻译： 提供了对象分类的系统和方法。 一个对象与描述该对象的属性一起被识别。 这些属性分为属性模式。 也可以在分类中使用的类别。 对于每个识别的类，使用已知分类的已知对象来创建并训练包含多个并行哈希表的草图表。 对于要分类的对象，使用每个草图表的所有散列函数处理每个属性模式。 这导致单个属性模式下每个草图下的多个值。 为每个草图表选择最低值。 针对每个属性模式评估所有草图表中的值分布。 这为每个属性模式产生歧视力。 选择具有高于给定阈值的辨别力的那些属性模式。 添加所选的属性模式和关联的草图表值。 识别具有最大总和的草图，并将与该草图表相关联的类分配给属性模式所属的对象。