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    Unsupervised method for classifying seasonal patterns
    13.
    发明授权
    Unsupervised method for classifying seasonal patterns 有权

    公开(公告)号：US10733528B2

    公开(公告)日：2020-08-04

    申请号：US15057062

    申请日：2016-02-29

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong

    IPC: G06N20/00

    Abstract: Techniques are described for classifying seasonal patterns in a time series. In an embodiment, a set of time series data is decomposed to generate a noise signal and a dense signal, where the noise signal includes a plurality of sparse features from the set of time series data and the dense signal includes a plurality of dense features from the set of time series data. A set of one or more sparse features from the noise signal is selected for retention. After selecting the sparse features, a modified set of time series data is generated by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features. At least one seasonal pattern is identified from the modified set of time series data. A summary for the seasonal pattern may then be generated and stored.

    System for detecting and characterizing seasons
    15.
    发明授权
    System for detecting and characterizing seasons 有权

    公开(公告)号：US10331802B2

    公开(公告)日：2019-06-25

    申请号：US15057065

    申请日：2016-02-29

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong ,  Amit Ganesh

    IPC: G06F17/30 ,  G06F17/18 ,  G06F21/55 ,  G06Q10/04 ,  G06N99/00 ,  G06Q10/06

    Abstract: Techniques are described for characterizing and summarizing seasonal patterns detected within a time series. A set of time series data is analyzed to identify a plurality of instances of a season, where each instance corresponds to a respective sub-period within the season. A first set of instances from the plurality of instances are associated with a particular class of seasonal pattern. After classifying the first set of instances, a second set of instances may remain unclassified or otherwise may not be associated with the particular class of seasonal pattern. Based on the first and second set of instances, a summary may be generated that identifies one or more stretches of time that are associated with the particular class of seasonal pattern. The one or more stretches of time may span at least one sub-period corresponding to at least one instance in the second set of instances.

    STATELESS DETECTION OF OUT-OF-MEMORY EVENTS IN VIRTUAL MACHINES
    16.
    发明申请
    STATELESS DETECTION OF OUT-OF-MEMORY EVENTS IN VIRTUAL MACHINES 审中-公开
    无条件检测虚拟机中的无记忆事件

    公开(公告)号：US20160371181A1

    公开(公告)日：2016-12-22

    申请号：US14743817

    申请日：2015-06-18

    Applicant: Oracle International Corporation

    Inventor： Dustin R. Garvey ,  Sampanna S. Salunke ,  Lik Wong ,  Xuemei Gao ,  Yongqiang Zhang ,  Eric S. Chan ,  Kenny C. Gross

    IPC: G06F12/02 ,  G06F3/06

    CPC classification number: G06F12/0253 ,  G06F3/0619 ,  G06F3/0653 ,  G06F3/0671

    Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.

    Abstract translation: 所公开的实施例提供了一种检测虚拟机中的异常事件的系统。 在运行期间，系统获取计算机系统中虚拟机执行期间收集的时间序列垃圾收集（GC）数据。 接下来，系统从时间序列GC数据生成一个或多个季节特征。 然后，系统使用顺序分析技术来分析时间序列GC数据以及虚拟机的GC活动中的异常的一个或多个季节特征。 最后，系统至少部分地基于识别虚拟机的GC活动中的异常来存储针对虚拟机的潜在的内存不足（OOM）事件的指示。

    CONFIGURATION AND MANAGEMENT OF REPLICATION UNITS FOR ASYNCHRONOUS DATABASE TRANSACTION REPLICATION
    18.
    发明公开
    CONFIGURATION AND MANAGEMENT OF REPLICATION UNITS FOR ASYNCHRONOUS DATABASE TRANSACTION REPLICATION 审中-公开

    公开(公告)号：US20240126782A1

    公开(公告)日：2024-04-18

    申请号：US18372005

    申请日：2023-09-22

    Applicant: Oracle International Corporation

    Inventor： Lik Wong ,  Leonid Novak ,  Sampanna Salunke ,  Mark Dilman ,  Wei-Ming Hu

    IPC: G06F16/27

    CPC classification number: G06F16/273

    Abstract: A consensus protocol-based replication approach is provided. Chunks are grouped into replication units (RUs) to optimize replication efficiency. Chunks may be assigned to RUs based on load and replication throughput. Splitting and merging RUs do not interrupt concurrent user workload or require routing changes. Transactions spanning chunks within an RU do not require distributed transaction processing. Each replication unit has a replication factor (RF), which refers to the number of copies/replicas of the replication unit, and an associated distribution factor (DF), which refers to the number of servers taking over the workload from a failed leader server. RUs may be placed in rings of servers, where the number of servers in a ring is equal to the replication factor, and quiescing the workload can be restricted to a ring of servers instead of the entire database.

    Unsupervised method for classifying seasonal patterns
    19.
    发明授权
    Unsupervised method for classifying seasonal patterns 有权

    公开(公告)号：US11836162B2

    公开(公告)日：2023-12-05

    申请号：US16862496

    申请日：2020-04-29

    Applicant: Oracle International Corporation

    Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong

    IPC: G06F16/28 ,  G06N20/00

    CPC classification number: G06F16/285 ,  G06N20/00

    Abstract: Techniques are described for classifying seasonal patterns in a time series. In an embodiment, a set of time series data is decomposed to generate a noise signal and a dense signal, where the noise signal includes a plurality of sparse features from the set of time series data and the dense signal includes a plurality of dense features from the set of time series data. A set of one or more sparse features from the noise signal is selected for retention. After selecting the sparse features, a modified set of time series data is generated by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features. At least one seasonal pattern is identified from the modified set of time series data. A summary for the seasonal pattern may then be generated and stored.

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