公开(公告)号：US10692255B2

公开(公告)日：2020-06-23

申请号：US16144831

申请日：2018-09-27

Applicant: Oracle International Corporation

Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong ,  Maria Kaval

IPC: G06T11/20 ,  G06Q30/02 ,  G06N20/00 ,  G06Q10/04 ,  G06F17/18 ,  G06F21/55 ,  G06Q10/06 ,  G06K9/00 ,  G06K9/62 ,  G06Q10/10 ,  G06T11/00

Abstract: Techniques are described for generating period profiles. According to an embodiment, a set of time series data is received, where the set of time series data includes data spanning a plurality of time windows having a seasonal period. Based at least in part on the set of time-series data, a first set of sub-periods of the seasonal period is associated with a particular class of seasonal pattern. A profile for a seasonal period that identifies which sub-periods of the seasonal period are associated with the particular class of seasonal pattern is generated and stored, in volatile or non-volatile storage. Based on the profile, a visualization is generated for at least one sub-period of the first set of sub-periods of the seasonal period that indicates that the at least one sub-period is part of the particular class of seasonal pattern.

公开(公告)号：US20190228022A1

公开(公告)日：2019-07-25

申请号：US16370227

申请日：2019-03-29

Applicant: Oracle International Corporation

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

IPC: G06F16/28 ,  G06F21/55 ,  G06F16/2458 ,  G06F17/18 ,  G06Q10/04

Abstract: Techniques are described for characterizing and summarizing seasonal patterns detected within a time series. According to an embodiment, 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.

公开(公告)号：US10282459B2

公开(公告)日：2019-05-07

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.

公开(公告)号：US20190114244A1

公开(公告)日：2019-04-18

申请号：US16213152

申请日：2018-12-07

Applicant: Oracle International Corporation

Inventor： Sampanna Salunke ,  Dustin Garvey ,  Uri Shaft ,  Lik Wong

IPC: G06F11/34 ,  G06F11/07

Abstract: Techniques are described for modeling variations in correlation to facilitate analytic operations. In one or more embodiments, at least one computing device receives first metric data that tracks a first metric for a first target resource and second metric data that tracks a second metric for a second target resource. In response to receiving the first metric data and the second metric data, the at least one computing device generates a time-series of correlation values that tracks correlation between the first metric and the second metric over time. Based at least in part on the time-series of correlation data, an expected correlation is determined and compared to an observed correlation. If the observed correlation falls outside of a threshold range or otherwise does not satisfy the expected correlation, then an alert and/or other output may be generated.

公开(公告)号：US20190035123A1

公开(公告)日：2019-01-31

申请号：US16144831

申请日：2018-09-27

Applicant: Oracle International Corporation

Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong ,  Maria Kaval

IPC: G06T11/20 ,  G06T11/00 ,  G06Q10/10

CPC classification number: G06T11/206 ,  G06F17/18 ,  G06F21/55 ,  G06K9/00536 ,  G06K9/628 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q10/0631 ,  G06Q10/06315 ,  G06Q10/1093 ,  G06Q30/0202 ,  G06T11/001

Abstract: Techniques are described for generating period profiles. According to an embodiment, a set of time series data is received, where the set of time series data includes data spanning a plurality of time windows having a seasonal period. Based at least in part on the set of time-series data, a first set of sub-periods of the seasonal period is associated with a particular class of seasonal pattern. A profile for a seasonal period that identifies which sub-periods of the seasonal period are associated with the particular class of seasonal pattern is generated and stored, in volatile or non-volatile storage. Based on the profile, a visualization is generated for at least one sub-period of the first set of sub-periods of the seasonal period that indicates that the at least one sub-period is part of the particular class of seasonal pattern.

公开(公告)号：US10127695B2

公开(公告)日：2018-11-13

申请号：US15445763

申请日：2017-02-28

Applicant: Oracle International Corporation

Inventor： Dustin Garvey ,  Uri Shaft ,  Lik Wong ,  Maria Kaval

IPC: G06T11/20 ,  G06Q10/10 ,  G06T11/00

Abstract: Techniques are described for generating period profiles. According to an embodiment, a set of time series data is received, where the set of time series data includes data spanning a plurality of time windows having a seasonal period. Based at least in part on the set of time-series data, a first set of sub-periods of the seasonal period is associated with a particular class of seasonal pattern. A profile for a seasonal period that identifies which sub-periods of the seasonal period are associated with the particular class of seasonal pattern is generated and stored, in volatile or non-volatile storage. Based on the profile, a visualization is generated for at least one sub-period of the first set of sub-periods of the seasonal period that indicates that the at least one sub-period is part of the particular class of seasonal pattern.

公开(公告)号：US10073906B2

公开(公告)日：2018-09-11

申请号：US15140358

申请日：2016-04-27

Applicant: Oracle International Corporation

Inventor： Edwina Lu ,  Dustin Garvey ,  Sampanna Salunke ,  Lik Wong ,  Aleksey Urmanov

IPC: G06F17/30

CPC classification number: G06F16/285

Abstract: Techniques are described for performing cluster analysis on a set of data points using tri-point arbitration. In one embodiment, a first cluster that includes a set of data points is generated within volatile and/or non-volatile storage of a computing device. A set of tri-point arbitration similarity values are computed where each similarity value in the set of similarity values corresponds to a respective data point pair and is computed based, at least in part, on a distance between the respective data point pair and a set of one or more arbiter data points. The first cluster is partitioned within volatile and/or non-volatile storage of the computing device into a set of two or more clusters. A determination is made, based at least in part on the set of similarity values in the tri-arbitration similarity matrix, whether to continue partitioning the set of data points.

公开(公告)号：US20180039555A1

公开(公告)日：2018-02-08

申请号：US15609938

申请日：2017-05-31

Applicant: Oracle International Corporation

Inventor： Sampanna Shahaji Salunke ,  Dustin Garvey ,  Uri Shaft ,  Maria Kaval

IPC: G06F11/34 ,  G06F11/30 ,  G06F11/32

CPC classification number: G06F11/3409 ,  G06F11/00 ,  G06F11/3034 ,  G06F11/3065 ,  G06F11/327

Abstract: Systems and methods for performing unsupervised baselining and anomaly detection using time-series data are described. In one or more embodiments, a baselining and anomaly detection system receives a set of time-series data. Based on the set of time-series, the system generates a first interval that represents a first distribution of sample values associated with the first seasonal pattern and a second interval that represents a second distribution of sample values associated with the second seasonal pattern. The system then monitors a time-series signals using the first interval during a first time period and the second interval during a second time period. In response to detecting an anomaly in the first seasonal pattern or the second seasonal pattern, the system generates an alert.

公开(公告)号：US20170249648A1

公开(公告)日：2017-08-31

申请号：US15266971

申请日：2016-09-15

Applicant: Oracle International Corporation

Inventor： Dustin Garvey ,  Uri Shaft ,  Edwina Ming-Yue Lu ,  Sampanna Shahaji Salunke ,  Lik Wong

IPC: G06Q30/02

CPC classification number: G06T11/206 ,  G06F17/18 ,  G06F21/55 ,  G06K9/00536 ,  G06K9/628 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q10/0631 ,  G06Q10/06315 ,  G06Q10/1093 ,  G06Q30/0202 ,  G06T11/001

Abstract: Techniques are described for generating seasonal forecasts. According to an embodiment, a set of time-series data is associated with one or more classes, which may include a first class that represent a dense pattern that repeats over multiple instances of a season in the set of time-series data and a second class that represent another pattern that repeats over multiple instances of the season in the set of time-series data. A particular class of data is associated with at least two sub-classes of data, where a first sub-class represents high data points from the first class, and a second sub-class represents another set of data points from the first class. A trend rate is determined for a particular sub-class. Based at least in part on the trend rate, a forecast is generated.