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    • 12. 发明申请
    • SYNCHRONOUS PARALLEL PIXEL PROCESSING FOR SCALABLE COLOR REPRODUCTION SYSTEMS
    • 用于可扩展颜色再现系统的同步并行像素处理
    • US20120200580A1
    • 2012-08-09
    • US13023798
    • 2011-02-09
    • Shanmuga-Nathan GnanasambandamLalit Keshav Mestha
    • Shanmuga-Nathan GnanasambandamLalit Keshav Mestha
    • G06F15/80G06T1/00
    • G06T1/20
    • What is disclosed is a novel system and method for parallel processing of intra-image data in a distributed computing environment. A generic architecture and method are presented which collectively facilitate image segmentation and block sorting and merging operations with a certain level of synchronization in a parallel image processing environment which has been traditionally difficult to parallelize. The present system and method enables pixel-level processing at higher speeds thus making it a viable service for a print/copy job document reproduction environment. The teachings hereof have been simulated on a cloud-based computing environment with a demonstrable increase of ≈2× with nominal 8-way parallelism, and an increase of ≈20×-100× on a graphics processor. In addition to production and office scenarios where intra-image processing are likely to be performed, these teachings are applicable to other domains where high-speed video and audio processing are desirable.
    • 公开的是用于在分布式计算环境中并行处理图像内数据的新型系统和方法。 提出了一种通常的架构和方法,其在传统上难以并行化的并行图像处理环境中共同促进图像分割和块排序和合并操作与一定水平的同步。 本系统和方法能够以更高的速度进行像素级处理,从而使其成为打印/复印作业文档再现环境的可行服务。 本文的教导已经在基于云计算环境下进行了模拟,具有标称8路并行性的≈2×的可见增加,并且在图形处理器上增加了≈20×-100×。 除了可能执行图像内处理的生产和办公场景之外,这些教导也适用于需要高速视频和音频处理的其他领域。
    • 14. 发明授权
    • Systems and methods for behavioral pattern mining
    • 行为模式挖掘的系统和方法
    • US09305104B2
    • 2016-04-05
    • US13529111
    • 2012-06-21
    • Changjun WuShanmuga-Nathan GnanasambandamGueyoung JungShi Zhao
    • Changjun WuShanmuga-Nathan GnanasambandamGueyoung JungShi Zhao
    • G06F7/00G06F17/00G06F17/30
    • G06F17/30876G06F17/30867
    • Methods and systems of performing data mining may include receiving a plurality of web log records and a plurality of call log records; associating one or more web log records with a call log record, wherein the associated user for each of the associated one or more web log records and the call log record are the same; identifying one or more patterns among the web log records for the plurality of call log records, wherein each pattern comprises one or more web accesses, a time stamp at which each of the one or more web accesses is performed and the call topic for the call log record; identifying one or more web log records associated with a new call, and predicting a call topic for the new call based on at least one pattern and the one or more web log records.
    • 执行数据挖掘的方法和系统可以包括接收多个web日志记录和多个呼叫记录记录; 将一个或多个Web日志记录与呼叫记录记录相关联,其中,所述相关联的一个或多个web日志记录中的每一个的关联用户和所述呼叫记录记录是相同的; 识别用于所述多个呼叫记录记录的所述web日志记录中的一个或多个模式,其中每个模式包括一个或多个web访问,执行所述一个或多个web访问中的每一个的时间戳以及所述呼叫的呼叫话题 日志记录; 识别与新呼叫相关联的一个或多个web日志记录,以及基于至少一个模式和所述一个或多个web日志记录来预测新呼叫的呼叫主题。
    • 15. 发明授权
    • Systems and methods for self-adaptive episode mining under the threshold using delay estimation and temporal division
    • 使用延迟估计和时间分割在阈值下进行自适应事件挖掘的系统和方法
    • US08965830B2
    • 2015-02-24
    • US13474083
    • 2012-05-17
    • Gueyoung JungShanmuga-Nathan Gnanasambandam
    • Gueyoung JungShanmuga-Nathan Gnanasambandam
    • G06F17/00G06N5/02
    • G06F17/30539
    • Embodiments relate to systems and methods for self-adaptive episode mining under time threshold using delay estimation and temporal division. An episode mining engine can analyze a set of episodes captured from a set of network resources to detect all sequences of user-specified frequency within a supplied runtime budget or time threshold. The engine can achieve desired levels of completeness in the results by mining the input log file in multiple stages or steps, each having successively longer lengths of event sequences. After completion of each stage, the engine calculates a remaining amount of runtime budget, and updates the amount of time to be allocated for each of the remaining stages up to a generated maximum stage (or sequence length). The engine thus corrects the estimated remaining time in the runtime budget (or threshold) after each stage, and continues to the next stage until the runtime budget is consumed.
    • 实施例涉及使用延迟估计和时间划分在时间阈值下进行自适应事件挖掘的系统和方法。 情节挖掘引擎可以分析从一组网络资源捕获的一组剧集,以检测所提供的运行时预算或时间阈值内的用户指定频率的所有序列。 引擎可以通过以多个阶段或步骤挖掘输入日志文件来获得所需结果的完整性,每个步骤具有连续更长的事件序列长度。 在每个阶段完成之后,引擎计算运行时间预算的剩余量,并且更新要分配给每个剩余阶段的时间直到生成的最大阶段(或序列长度)。 因此,引擎在每个阶段之后校正运行时间预算(或阈值)中的估计剩余时间,并且继续下一阶段,直到运行时预算消耗。
    • 16. 发明申请
    • SYSTEMS AND METHODS FOR SELF-ADAPTIVE EPISODE MINING UNDER THE THRESHOLD USING DELAY ESTIMATION AND TEMPORAL DIVISION
    • 使用延迟估计和时间段在自适应阈值下进行自适应压缩采矿的系统和方法
    • US20130311994A1
    • 2013-11-21
    • US13474083
    • 2012-05-17
    • Gueyoung JungShanmuga-Nathan Gnanasambandam
    • Gueyoung JungShanmuga-Nathan Gnanasambandam
    • G06F9/46
    • G06F17/30539
    • Embodiments relate to systems and methods for self-adaptive episode mining under time threshold using delay estimation and temporal division. An episode mining engine can analyze a set of episodes captured from a set of network resources to detect all sequences of user-specified frequency within a supplied runtime budget or time threshold. The engine can achieve desired levels of completeness in the results by mining the input log file in multiple stages or steps, each having successively longer lengths of event sequences. After completion of each stage, the engine calculates a remaining amount of runtime budget, and updates the amount of time to be allocated for each of the remaining stages up to a generated maximum stage (or sequence length). The engine thus corrects the estimated remaining time in the runtime budget (or threshold) after each stage, and continues to the next stage until the runtime budget is consumed.
    • 实施例涉及使用延迟估计和时间划分在时间阈值下进行自适应事件挖掘的系统和方法。 情节挖掘引擎可以分析从一组网络资源捕获的一组剧集,以检测所提供的运行时预算或时间阈值内的用户指定频率的所有序列。 引擎可以通过以多个阶段或步骤挖掘输入日志文件来获得所需结果的完整性,每个步骤具有连续更长的事件序列长度。 在每个阶段完成之后,引擎计算运行时间预算的剩余量,并且更新要分配给每个剩余阶段的时间直到生成的最大阶段(或序列长度)。 因此,引擎在每个阶段之后校正运行时间预算(或阈值)中的估计剩余时间,并且继续下一阶段,直到运行时预算消耗。