会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 2. 发明授权
    • Transformation of directed acyclic graph query plans to linear query plans
    • 将有向非循环图查询计划转换为线性查询计划
    • US08260768B2
    • 2012-09-04
    • US12697093
    • 2010-01-29
    • Song WangChetan Kumar GuptaAbhay Mehta
    • Song WangChetan Kumar GuptaAbhay Mehta
    • G06F17/30G06F7/00
    • G06F17/30
    • Methods, computer-readable storage media and computer systems are provided for transforming a directed acyclic graph (“DAG”) query plan into a linear plan. A DAG query plan may include a first operator and a second operator that are scheduled to be executed in parallel. The DAG query plan may be modified so that the first and second operators are executed in series as an upstream operator and a downstream operator. A data unit output from the upstream operator may be marked to indicate that the data unit has been processed by the upstream operator. The data unit received as input at the downstream operator may be inspected to determine whether the data unit has been marked. Once in linear form, the query plan may be optimized to conserve computing resources.
    • 提供了一种方法,计算机可读存储介质和计算机系统,用于将有向非循环图(“DAG”)查询计划转换为线性计划。 DAG查询计划可以包括被调度为并行执行的第一运算符和第二运算符。 可以修改DAG查询计划,使得第一和第二运算符作为上游运算符和下游运算符串联执行。 可以标记来自上游运营商的数据单元输出,以指示数据单元已被上游运营商处理。 可以检查在下游操作者处接收作为输入的数据单元以确定数据单元是否已被标记。 一旦成为线性形式,查询计划可以被优化以节省计算资源。
    • 4. 发明申请
    • DATA CLASSIFICATION METHOD FOR UNKNOWN CLASSES
    • 未知类别的数据分类方法
    • US20100198758A1
    • 2010-08-05
    • US12364442
    • 2009-02-02
    • Chetan Kumar GuptaAbhay MehtaSong Wang
    • Chetan Kumar GuptaAbhay MehtaSong Wang
    • G06F15/18
    • G06N20/00
    • A system and method for creating a CD Tree for data having unknown classes are provided. Such a method can include dividing training data into a plurality of subsets of node training data at a plurality of nodes arranged in a hierarchical arrangement, wherein the node training data has a range. Furthermore, dividing node training data at each node can include, ordering the node training data, generating a plurality of separation points and a plurality of pairs of bins from the node training data, wherein each pair of bins includes a first bin and a second bin with a separation point being located between the first bin and the second bin, and classifying the node training data into either the first bin or the second bin for each of the separation points, wherein the classifying is based on a data classifier. Validation data can be utilized to calculate the bin accuracy between the node training data bin pairs and the validation data bin pairs for each separation point, and the separation point having a high bin accuracy can be selected as the node separation point.
    • 提供了一种用于为具有未知类的数据创建CD树的系统和方法。 这种方法可以包括将训练数据划分为以分层布置排列的多个节点的节点训练数据的多个子集,其中节点训练数据具有范围。 此外,在每个节点处划分节点训练数据可以包括:从节点训练数据生成节点训练数据,生成多个分离点和多对分组,其中每对分组包括第一分组和第二分组 其中分离点位于第一仓和第二仓之间,并且将节点训练数据分类为用于每个分离点的第一仓或第二仓,其中分类基于数据分类器。 可以使用验证数据来计算节点训练数据箱对与每个分离点的验证数据箱对之间的仓精度,并且可以选择具有高仓精度的分离点作为节点分离点。
    • 7. 发明授权
    • Event prediction
    • 事件预测
    • US08660979B2
    • 2014-02-25
    • US13040171
    • 2011-03-03
    • Chetan Kumar GuptaSong WangAbhay Mehta
    • Chetan Kumar GuptaSong WangAbhay Mehta
    • G06F9/44G06N7/02G06N7/06
    • G06N7/005
    • A selected set of one or more first events that have occurred within current data is received. An episode set of which the selected set is a subset, the episode set including one or more second events that have occurred within historical data related to the current data is identified. One or more third events that occurred within the historical data within a predetermined time horizon after the one or more second events of the episode set occurred within the historical data are identified. The one or more third events are predicted to likely occur within the current data as a result of the one or more first events having occurred within the current data.
    • 接收在当前数据内发生的所选择的一个或多个第一事件集合。 识别所选集合是其子集的集集,包括在与当前数据相关的历史数据内发生的一个或多个第二事件的剧集集。 识别发生在历史数据中的发生集合的一个或多个第二事件之后的预定时间范围内在历史数据内发生的一个或多个第三事件。 由于一个或多个第一事件已经在当前数据内发生,所以一个或多个第三事件被预测可能发生在当前数据内。