公开(公告)号：US11455284B2

公开(公告)日：2022-09-27

申请号：US16564910

申请日：2019-09-09

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Michael Zoll ,  Yaser I. Suleiman ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/30 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  H04L41/142 ,  G06N7/00 ,  H04L41/069 ,  G06N20/00 ,  H04L41/0695 ,  H04L41/0823 ,  H04L43/02

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

公开(公告)号：US10409789B2

公开(公告)日：2019-09-10

申请号：US15707500

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Michael Zoll ,  Yaser I. Suleiman ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/30 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N7/00 ,  H04L12/24 ,  G06N20/20 ,  G06N20/00 ,  H04L12/26

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

公开(公告)号：US20230155956A1

公开(公告)日：2023-05-18

申请号：US17979688

申请日：2022-11-02

Applicant: Oracle International Corporation

Inventor： Joshua Deen Griffin ,  Wataru Miyoshi

IPC: H04L47/78 ,  H04L47/80 ,  H04L47/762

CPC classification number: H04L47/781 ,  H04L47/805 ,  H04L47/762 ,  H04L47/803

Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

公开(公告)号：US11502971B1

公开(公告)日：2022-11-15

申请号：US17454940

申请日：2021-11-15

Applicant: Oracle International Corporation

Inventor： Joshua Deen Griffin ,  Wataru Miyoshi

IPC: H04L47/78 ,  H04L47/80 ,  H04L47/762

Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

公开(公告)号：US12047305B2

公开(公告)日：2024-07-23

申请号：US17979688

申请日：2022-11-02

Applicant: Oracle International Corporation

Inventor： Joshua Deen Griffin ,  Wataru Miyoshi

IPC: H04L47/78 ,  H04L47/762 ,  H04L47/80

CPC classification number: H04L47/781 ,  H04L47/762 ,  H04L47/803 ,  H04L47/805

Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

公开(公告)号：US10997135B2

公开(公告)日：2021-05-04

申请号：US15707536

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Michael Zoll ,  Yaser I. Suleiman ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: H04L12/24 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  G06N7/00 ,  G06N20/00 ,  H04L12/26

Abstract: Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

公开(公告)号：US11308049B2

公开(公告)日：2022-04-19

申请号：US15707454

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Yaser I. Suleiman ,  Michael Zoll ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/21 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  H04L41/142 ,  G06N7/00 ,  H04L41/069 ,  G06N20/00 ,  H04L41/0695 ,  H04L41/0823 ,  H04L43/02

Abstract: Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

公开(公告)号：US10909095B2

公开(公告)日：2021-02-02

申请号：US15707417

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Yaser I. Suleiman ,  Michael Zoll ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/00 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  H04L12/24 ,  G06N7/00 ,  G06N20/00 ,  H04L12/26

Abstract: Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.