公开(公告)号：US20220107933A1

公开(公告)日：2022-04-07

申请号：US17060999

申请日：2020-10-01

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Mayur Bency ,  Marc Jolles ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/23 ,  G06F16/245

Abstract: Systems and methods for adjusting parameters for a spin-lock implementation of concurrency control are described herein. In an embodiment, a system continuously retrieves, from a resource management system, one or more state values defining a state of the resource management system. Based on the one or more state values, the system determines that the resource management system has reached a steady state and, in response adjusts a plurality of parameters for spin-locking performed by said resource management system to identify optimal values for the plurality of parameters. After adjusting the plurality of parameters, the system detects, based on one or more current state values, a workload change in the resource management system and, in response, readjusts the plurality of parameters for spin-locking performed by said resource management system to identify new optimal values for the parameters.

公开(公告)号：US10768982B2

公开(公告)日：2020-09-08

申请号：US16135802

申请日：2018-09-19

Applicant: Oracle International Corporation

Inventor： Andrew Brownsword ,  Tayler Hetherington ,  Pavan Chandrashekar ,  Akhilesh Singhania ,  Stuart Wray ,  Pravin Shinde ,  Felix Schmidt ,  Craig Schelp ,  Onur Kocberber ,  Juan Fernandez Peinador ,  Rod Reddekopp ,  Manel Fernandez Gomez ,  Nipun Agarwal

IPC: G06F9/54 ,  G06F9/48 ,  G06F11/30 ,  G06F9/38 ,  G06F9/455 ,  G06N20/00 ,  G06F16/11 ,  G06F16/18

Abstract: Herein are techniques for analysis of data streams. In an embodiment, a computer associates each software actor with data streams. Each software actor has its own backlog queue of data to analyze. In response to receiving some stream content and based on the received stream content, data is distributed to some software actors. In response to determining that the data satisfies completeness criteria of a particular software actor, an indication of the data is appended onto the backlog queue of the particular software actor. The particular software actor is reset to an initial state by loading an execution snapshot of a previous initial execution of an embedded virtual machine. Based on the particular software actor, execution of the execution snapshot of the previous initial execution is resumed to dequeue and process the indication of the data from the backlog queue of the particular software actor to generate a result.

公开(公告)号：US11620547B2

公开(公告)日：2023-04-04

申请号：US16877882

申请日：2020-05-19

Applicant: Oracle International Corporation

Inventor： Tomas Karnagel ,  Onur Kocberber ,  Farhan Tauheed ,  Nipun Agarwal

IPC: G06N5/04 ,  G06N20/00

Abstract: Techniques for estimating the number of distinct values in a data set using machine learning are provided. In one technique, a sample of a data set is retrieved where the sample is a strict subset of the data set. The sample is analyzed to identify feature values of multiple features of the sample. The feature values are inserted into a machine-learned model that computes a prediction regarding a number of distinct values in the data set. An estimated number of distinct values that is based on the prediction is stored in association with the data set.

公开(公告)号：US20230022884A1

公开(公告)日：2023-01-26

申请号：US17381072

申请日：2021-07-20

Applicant: Oracle International Corporation

Inventor： Peyman Faizian ,  Mayur Bency ,  Onur Kocberber ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F12/0842 ,  G06F16/22

Abstract: Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.

公开(公告)号：US20210406717A1

公开(公告)日：2021-12-30

申请号：US16914816

申请日：2020-06-29

Applicant: Oracle International Corporation

Inventor： Farhan Tauheed ,  Onur Kocberber ,  Tomas Karnagel ,  Nipun Agarwal

IPC: G06N5/04 ,  G06N20/00 ,  G06F16/22

Abstract: Herein are approaches for self-optimization of a database management system (DBMS) such as in real time. Adaptive just-in-time sampling techniques herein estimate database content statistics that a machine learning (ML) model may use to predict configuration settings that conserve computer resources such as execution time and storage space. In an embodiment, a computer repeatedly samples database content until a dynamic convergence criterion is satisfied. In each iteration of a series of sampling iterations, a subset of rows of a database table are sampled, and estimates of content statistics of the database table are adjusted based on the sampled subset of rows. Immediately or eventually after detecting dynamic convergence, a machine learning (ML) model predicts, based on the content statistic estimates, an optimal value for a configuration setting of the DBMS.

公开(公告)号：US11082438B2

公开(公告)日：2021-08-03

申请号：US16122398

申请日：2018-09-05

Applicant: Oracle International Corporation

Inventor： Juan Fernandez Peinador ,  Manel Fernandez Gomez ,  Guang-Tong Zhou ,  Hossein Hajimirsadeghi ,  Andrew Brownsword ,  Onur Kocberber ,  Felix Schmidt ,  Craig Schelp

IPC: H04L29/06 ,  G06N3/04 ,  G06K9/62 ,  G06F16/80

Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.

公开(公告)号：US10892961B2

公开(公告)日：2021-01-12

申请号：US16271535

申请日：2019-02-08

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Felix Schmidt ,  Craig Schelp ,  Pravin Shinde

IPC: H04L12/24 ,  H04L12/26 ,  G06F9/455 ,  H04L29/08

Abstract: Herein are computerized techniques for autonomous and artificially intelligent administration of a computer cloud health monitoring system. In an embodiment, an orchestration computer automatically detects a current state of network elements of a computer network by processing: a) a network plan that defines a topology of the computer network, and b) performance statistics of the network elements. The network elements include computers that each hosts virtual execution environment(s). Each virtual execution environment hosts analysis logic that transforms raw performance data of a network element into a portion of the performance statistics. For each computer, a configuration specification for each virtual execution environment of the computer is automatically generated based on the network plan and the current state of the computer network. At least one virtual execution environment is automatically tuned and/or re-provisioned based on a generated configuration specification.

公开(公告)号：US12248444B1

公开(公告)日：2025-03-11

申请号：US18539928

申请日：2023-12-14

Applicant: Oracle International Corporation

Inventor： Fotis Savva ,  Farhan Tauheed ,  Marc Jolles ,  Onur Kocberber ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/20 ,  G06F16/21 ,  G06F16/2455 ,  G06F16/28

Abstract: Auto-parallel-load techniques are provided for automatically loading database objects from an on-disk database system into an in-memory database system. The auto-parallel-load techniques involve a pipeline that includes several components. In one implementation, each of the pipeline components is configured to receive, extract information from, and add information to, a “state object”. One or more of the pipeline components include logic that is based on the output of a corresponding machine learning model. The machine learning models used by the pipeline components may be trained from training sets from which outliers have been excluded, and may be used as the basis for generating linear models that are used during runtime, to produce estimates that affect the parameters of the auto-parallel-load operation.

公开(公告)号：US12229135B2

公开(公告)日：2025-02-18

申请号：US17699607

申请日：2022-03-21

Applicant: Oracle International Corporation

Inventor： Urvashi Oswal ,  Jian Wen ,  Farhan Tauheed ,  Onur Kocberber ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/2453 ,  G06F11/34 ,  G06F16/21 ,  G06F16/22 ,  G06F16/27

Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automatic data placement recommendations for partitioning data across multiple nodes in a database system. The system is configured to extract workload-specific features of a database workload running at a database system and dataset-specific features of a database running on the database system. The workload-specific features characterize utilization of the database workload. The dataset-specific features characterize how data is organized within the database. The system identifies a plurality of candidate keys for determining how to partition data stored in the database across nodes. Based at least in part on the workload-specific features, the dataset specific features, and the plurality of candidate keys, a set of candidate key combinations for partitioning data is generated. Using a machine learning model, determine a particular candidate key combination that optimizes query execution performance benefit based on the workload-specific features and the dataset specific features. Generate data placement commands to allocate the database tables across the nodes.

公开(公告)号：US11531915B2

公开(公告)日：2022-12-20

申请号：US16359256

申请日：2019-03-20

Applicant: Oracle International Corporation

Inventor： Tayler Hetherington ,  Zahra Zohrevand ,  Onur Kocberber ,  Karoon Rashedi Nia ,  Sam Idicula ,  Nipun Agarwal

IPC: G06N5/04 ,  G06N20/00

Abstract: Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use.