公开(公告)号：US11797882B2

公开(公告)日：2023-10-24

申请号：US16691321

申请日：2019-11-21

Applicant: Oracle International Corporation

Inventor： Kenneth P. Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu

IPC: G06N20/00 ,  G06F16/2458 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F16/2474 ,  G06N7/01

Abstract: We describe a system that performs prognostic-surveillance operations based on an inferential model that dynamically adapts to evolving operational characteristics of a monitored asset. During a surveillance mode, the system receives a set of time-series signals gathered from sensors in the monitored asset. Next, the system uses an inferential model to generate estimated values for the set of time-series signals, and then performs a pairwise differencing operation between actual values and the estimated values for the set of time-series signals to produce residuals. Next, the system performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. When a tripping frequency of the SPRT alarms exceeds a threshold value, which is indicative of an incipient anomaly in the monitored asset, the system triggers an alert. While the prognostic-surveillance system is operating in the surveillance mode, the system incrementally updates the inferential model based on the time-series signals.

公开(公告)号：US20230061280A1

公开(公告)日：2023-03-02

申请号：US17462592

申请日：2021-08-31

Applicant: Oracle International Corporation

Inventor： James Charles Rohrkemper ,  Richard Paul Sonderegger ,  Anna Chystiakova ,  Kenneth Paul Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu ,  Guang Chao Wang

IPC: G06N20/00 ,  G06F16/242

Abstract: Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a “fault” or “no-fault” in the system. The set of data includes signals from multiple different data sources, such as sensors. The system applies an abductive model, generated based on the deterministic machine learning model, to the operational result. The abductive model identifies a particular set of data sources that is associated with the root cause of the operational result. The system generates a human-understandable explanation for the operational result based on the identified root cause.

公开(公告)号：US20220237509A1

公开(公告)日：2022-07-28

申请号：US17379937

申请日：2021-07-19

Applicant: Oracle International Corporation

Inventor： John Frederick Courtney ,  Kenneth Paul Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Guang Chao Wang ,  Anna Chystiakova ,  Richard Paul Sonderegger ,  Zhen Hua Liu

IPC: G06N20/00 ,  G06N5/04 ,  G06F11/32

Abstract: Techniques for providing decision rationales for machine-learning guided processes are described herein. In some embodiments, the techniques described herein include processing queries for an explanation of an outcome of a set of one or more decisions guided by one or more machine-learning processes with supervision by at least one human operator. Responsive to receiving the query, a system determines, based on a set of one or more rationale data structures, whether the outcome was caused by human operator error or the one or more machine-learning processes. The system then generates a query response indicating whether the outcome was caused by the human operator error or the one or more machine-learning processes.

公开(公告)号：US11036756B2

公开(公告)日：2021-06-15

申请号：US16518114

申请日：2019-07-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Christoph Bussler ,  Dieter Gawlick ,  Weiwei Gong

IPC: G06F16/00 ,  G06F16/25 ,  G06F16/22

Abstract: Techniques related to an in-memory key-value store for a multi-model database are disclosed. In an embodiment, a relational database may be maintained on persistent storage. The relational database may be managed by a database server and may include a database table. The database table may be stored in a persistent format. Key-value records may be generated within volatile memory accessible to the database server by converting data in the database table to a key-value format. The key-value format may be different from and independent of the persistent format. A database statement referencing the database table may be executed based on determining whether to access one or more key-value records in the volatile memory or to access the data in the database table. In response to determining to access the one or more key-value records, the database server may access the one or more key-value records in the volatile memory.

公开(公告)号：US10740310B2

公开(公告)日：2020-08-11

申请号：US15925427

申请日：2018-03-19

Applicant: Oracle International Corporation

Inventor： Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu ,  Adel Ghoneimy

IPC: G06F17/00 ,  G06F16/22 ,  G06N5/04 ,  G06N20/00 ,  G06F16/174 ,  G06F16/23 ,  G06F17/40 ,  G06F11/30 ,  G06F11/00

Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.

公开(公告)号：US20190286725A1

公开(公告)日：2019-09-19

申请号：US15925427

申请日：2018-03-19

Applicant: Oracle International Corporation

Inventor： Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu ,  Adel Ghoneimy

IPC: G06F17/30 ,  G06N99/00 ,  G06N5/04

Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.

公开(公告)号：US20190236162A1

公开(公告)日：2019-08-01

申请号：US15885600

申请日：2018-01-31

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Mengying Li ,  Dieter Gawlick ,  Zhen Hua Liu

IPC: G06F17/30

CPC classification number: G06F16/1744 ,  G06F16/2237 ,  G06F16/2453 ,  G06F16/24561

Abstract: The disclosed embodiments relate to a system that caches time-series data in a time-series database system. During operation, the system receives the time-series data, wherein the time-series data comprises a series of observations obtained from sensor readings for each signal in a set of signals. Next, the system performs a multivariate memory vectorization (MMV) operation on the time-series data, which selects a subset of observations in the time-series data that represents an underlying structure of the time-series data for individual and multivariate signals that comprise the time-series data. The system then performs a geometric compression aging (GAC) operation on the selected subset of time-series data. While subsequently processing a query involving the time-series data, the system: caches the selected subset of the time-series data in an in-memory database cache in the time-series database system; and accesses the selected subset of the time-series data from the in-memory database cache.

公开(公告)号：US20230008658A1

公开(公告)日：2023-01-12

申请号：US17368840

申请日：2021-07-07

Applicant: Oracle International Corporation

Inventor： Richard P. Sonderegger ,  Kenneth P. Baclawski ,  Guang C. Wang ,  Anna Chystiakova ,  Dieter Gawlick ,  Zhen Hua Liu ,  Kenny C. Gross

IPC: G05B23/02 ,  G06N20/00 ,  G05B13/02 ,  G05B15/02

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

公开(公告)号：US10740358B2

公开(公告)日：2020-08-11

申请号：US14665171

申请日：2015-03-23

Applicant: Oracle International Corporation

Inventor： Eric S. Chan ,  Dieter Gawlick ,  Adel Ghoneimy ,  Zhen Hua Liu

IPC: G06F16/28 ,  G06F16/27 ,  G06F16/9535 ,  G06F11/30 ,  G06F11/34 ,  G06F16/21

Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.

公开(公告)号：US20140310285A1

公开(公告)日：2014-10-16

申请号：US14109651

申请日：2013-12-17

Applicant: Oracle International Corporation

Inventor： Eric S. Chan ,  Dieter Gawlick ,  Adel Ghoneimy ,  Zhen Hua Liu

IPC: G06F17/30

CPC classification number: H04L41/5009 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

Abstract translation: 数据可以分为事实，信息，假设和指令。 通过应用可分类到分类，评估，决议和制定的知识，基于其他类别的数据生成某些类别的数据的活动。 活动可以通过分类评估 - 分配制度（CARE）控制引擎来驱动。 CARE控制和这些分类可用于增强大量系统，例如诊断系统，例如通过历史记录保存，机器学习和自动化。 这样的诊断系统可以包括基于将知识应用于诸如线程或堆栈段强度和内存堆使用的系统生命体征来预测计算系统故障的系统。 这些生命体征是可以分类以产生诸如记忆泄漏，车队效应或其他问题的信息的事实。 分类可以涉及自动生成类，状态，观察，预测，规范，目标以及具有不规则持续时间的采样间隔的处理。