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    • 2. 发明授权
    • Scheduling resources in a penalty-based environment
    • 在基于惩罚的环境中调度资源
    • US08302097B2
    • 2012-10-30
    • US11767891
    • 2007-06-25
    • Melissa Jane BucoRong Nickle ChangLaura Zaihua LuanChristopher WardJoel Leonard WolfPhilip Shi-lung Yu
    • Melissa Jane BucoRong Nickle ChangLaura Zaihua LuanChristopher WardJoel Leonard WolfPhilip Shi-lung Yu
    • G06F9/46
    • G06F9/4887G06Q10/06
    • The present invention relates to the problem of scheduling work for employees and/or other resources in a help desk or similar environment. The employees have different levels of training and availabilities. The jobs, which occur as a result of dynamically occurring events, consist of multiple tasks ordered by chain precedence. Each job and/or task carries with it a penalty which is a step function of the time taken to complete it, the deadlines and penalties having been negotiated as part of one or more service level agreement contracts. The goal is to minimize the total amount of penalties paid. The invention consists of a pair of heuristic schemes for this difficult scheduling problem, one greedy and one randomized. The greedy scheme is used to provide a quick initial solution, while the greedy and randomized schemes are combined in order to think more deeply about particular problem instances. The invention also includes a scheme for determining how much time to allocate to thinking about each of several potential problem instance variants.
    • 本发明涉及在帮助台或类似环境中调度员工和/或其他资源的工作的问题。 员工具有不同的培训水平和可用性。 由于动态发生的事件而发生的作业由链优先级排序的多个任务组成。 每项工作和/或任务带有罚款,这是完成它所需的时间的一个阶段功能,作为一个或多个服务级别协议合同的一部分,谈判达成的期限和处罚。 目标是尽量减少所支付的罚款总额。 本发明由一对启发式方案组成,用于这个困难的调度问题,一个是贪心的,一个是随机的。 贪心的方案用于提供一个快速的初步解决方案,而贪心和随机的方案是相结合的,以便更深入地思考特定的问题实例。 本发明还包括一种用于确定分配多少时间以考虑几个潜在问题实例变体中的每一个的方案。
    • 3. 发明授权
    • Structural data classification
    • 结构数据分类
    • US08121967B2
    • 2012-02-21
    • US12141251
    • 2008-06-18
    • Hong ChengWei FanXifeng YanPhilip Shi-lung Yu
    • Hong ChengWei FanXifeng YanPhilip Shi-lung Yu
    • G06F17/00G06N5/02
    • G06N99/005
    • Techniques for classifying structural data with skewed distribution are disclosed. By way of example, a method classifying structural input data comprises a computer system performing the following steps. Multiple classifiers are constructed, wherein each classifier is constructed on a subset of training data, using one or more selected composite features from the subset of training data. A consensus among the multiple classifiers is computed in accordance with a voting scheme such that at least a portion of the structural input data is assigned to a particular class in accordance with the computed consensus. Such techniques for structured data classification are capable of handling skewed class distribution and partial feature coverage issues.
    • 公开了分布具有偏斜分布的结构数据的技术。 作为示例,分类结构输入数据的方法包括执行以下步骤的计算机系统。 构建多个分类器,其中使用来自训练数据的子集的一个或多个选定的复合特征,在训练数据的子集上构建每个分类器。 根据投票方案计算多个分类器之间的共识,使得至少一部分结构输入数据根据所计算的一致性被分配给特定类别。 这种用于结构化数据分类的技术能够处理倾斜的类分布和部分特征覆盖问题。
    • 9. 发明申请
    • Method and Apparatus for Aggregation in Uncertain Data
    • 不确定数据聚合的方法和装置
    • US20090222472A1
    • 2009-09-03
    • US12039076
    • 2008-02-28
    • Charu C. AggarwalPhilip Shi-Lung Yu
    • Charu C. AggarwalPhilip Shi-Lung Yu
    • G06F17/30
    • G06F17/30489
    • Techniques are disclosed for aggregation in uncertain data in data processing systems. For example, a method of aggregation in an application that involves an uncertain data set includes the following steps. The uncertain data set along with uncertainty information is obtained. One or more clusters of data points are constructed from the data set. Aggregate statistics of the one or more clusters and uncertainty information are stored. The data set may be data from a data stream. It is realized that the use of even modest uncertainty information during an application such as a data mining process is sufficient to greatly improve the quality of the underlying results.
    • 公开了用于在数据处理系统中的不确定数据中聚合的技术。 例如,涉及不确定数据集的应用程序中的聚合方法包括以下步骤。 获得不确定性数据集以及不确定性信息。 从数据集构建一个或多个数据点簇。 存储一个或多个聚类和不确定性信息的聚合统计信息。 数据集可以是来自数据流的数据。 实现在诸如数据挖掘过程的应用中使用甚至适度的不确定性信息足以大大提高底层结果的质量。