公开(公告)号：US20240378305A1

公开(公告)日：2024-11-14

申请号：US18316787

申请日：2023-05-12

Applicant: Snowflake Inc.

Inventor： Suraj P. Acharya ,  Jennifer Wenjun Bi ,  Khalid Zaman Bijon ,  Damien Carru ,  Lin Chan ,  Tianyi Chen ,  Jeremy Yujui Chen ,  Thierry Cruanes ,  Benoit Dageville ,  Simon Holm Jensen ,  Boxin Jiang ,  Dmitry A. Lychagin ,  Subramanian Muralidhar ,  Shuaishuai Nie ,  Eric Robinson ,  Sahaj Saini ,  David Schultz ,  Kevin Wang ,  Wenqi Wei ,  Zixi Zhang ,  Xingzhe Zhou

IPC: G06F21/62 ,  G06F21/60

Abstract: Systems and methods for generating object references with selectable scopes are provided. The systems and methods perform operations including calling, by a first entity, a reference generator function using one or more arguments associated with a database object that the first entity is authorized to access according to a first set of access privileges, the one or more arguments comprising a scope definition that defines persistence of a reference. The operations include obtaining, from the reference generator function, a reference to the database object, the reference persisting according to the scope definition. The operations include passing the reference to a second entity to enable the second entity to perform one or more database operations on the database object according to a second set of access privileges derived from the first set of access privileges.

公开(公告)号：US11934927B2

公开(公告)日：2024-03-19

申请号：US18087518

申请日：2022-12-22

Applicant: SNOWFLAKE INC.

Inventor： Orestis Kostakis ,  Qiming Jiang ,  Boxin Jiang

IPC: G06N20/00 ,  G06F16/24

CPC classification number: G06N20/00 ,  G06F16/24

Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.

公开(公告)号：US11580251B1

公开(公告)日：2023-02-14

申请号：US17519729

申请日：2021-11-05

Applicant: SNOWFLAKE INC.

Inventor： Boxin Jiang ,  Qiming Jiang

IPC: G06F16/2455 ,  G06F21/62

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for redacting sensitive data within a database. An example method can include receiving a data query referencing unredacted data of a database, responsive to the data query, executing, by a processing device, a redaction operation to identify sensitive data within the unredacted data of the database, and returning a redacted data set in which the sensitive data is replaced or removed to the data query.

公开(公告)号：US20240232722A1

公开(公告)日：2024-07-11

申请号：US18582560

申请日：2024-02-20

Applicant: SNOWFLAKE INC.

Inventor： Orestis Kostakis ,  Qiming Jiang ,  Boxin Jiang

IPC: G06N20/00 ,  G06F16/24

CPC classification number: G06N20/00 ,  G06F16/24

Abstract: Techniques for managing input and output error of a machine learning (ML) model in a database system are presented herein. Test data is generated from successive versions of a database system, the database system comprising a machine learning (ML) model to generate an output corresponding to a function of the database system The test data is used to train an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system. In response to the ML model generating a first output based on a first input: the error model adjusts the first output when the error is associated with the output to the ML model and adjusts the first input when the error is associated with the input to the ML model.

公开(公告)号：US20240078220A1

公开(公告)日：2024-03-07

申请号：US18505908

申请日：2023-11-09

Applicant: SNOWFLAKE INC.

Inventor： Boxin Jiang ,  Qiming Jiang

IPC: G06F16/21 ,  G06F16/242 ,  G06F16/2458 ,  G06N3/08

CPC classification number: G06F16/217 ,  G06F16/2433 ,  G06F16/2474 ,  G06N3/08

Abstract: An example method of tuning a machine learning operation can include receiving a data query comprising a reference to an input data set of a database, generating a plurality of hyperparameter sets based on the input data set, in response to receiving the data query, training a plurality of machine learning models using the plurality of hyperpararneter sets, selecting a first mathine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model, and in response to receiving the data query, returning the output of the first machine learning model.

公开(公告)号：US20230153455A1

公开(公告)日：2023-05-18

申请号：US18155293

申请日：2023-01-17

Applicant: SNOWFLAKE INC.

Inventor： Boxin Jiang ,  Qiming Jiang

IPC: G06F21/62 ,  G06F16/2455

CPC classification number: G06F21/6227 ,  G06F16/24564

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for redacting sensitive data within a database. An example method can include receiving a data query referencing unredacted data of a database, wherein the data query that is received comprises a value identifying a type of sensitive data to be redacted from the unredacted data, responsive to the data query, executing, by a processing device, a redaction operation to identify sensitive data that matches the type within the unredacted data of the database, and returning a redacted data set in which the sensitive data that matches the type is replaced or removed to the data query.

公开(公告)号：US11609970B1

公开(公告)日：2023-03-21

申请号：US17877588

申请日：2022-07-29

Applicant: Snowflake Inc.

Inventor： Michel Adar ,  Boxin Jiang ,  Qiming Jiang ,  John Reumann ,  Boyu Wang ,  Jiaxun Wu

IPC: G06F17/18

Abstract: A processing device may analyze a set of time series data using a time series forecasting model comprising an attributes model and a trend detection model. The attributes model may comprise a modified gradient boosting decision tree (GBDT) based algorithm. Analyzing the set of time series data comprises determining a set of features of the set of time series data, the set of features including periodic components as well as arbitrary components. A trend of the set of time series data may be determined using the trend detection model and the set of features and the trend may be combined to generate a time series forecast.

公开(公告)号：US12026221B2

公开(公告)日：2024-07-02

申请号：US18112944

申请日：2023-02-22

Applicant: Snowflake Inc.

Inventor： Michel Adar ,  Boxin Jiang ,  Qiming Jiang ,  John Reumann ,  Boyu Wang ,  Jiaxun Wu

IPC: G06F17/18

CPC classification number: G06F17/18

Abstract: Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.

公开(公告)号：US20240062098A1

公开(公告)日：2024-02-22

申请号：US17821587

申请日：2022-08-23

Applicant: Snowflake Inc.

Inventor： Rachel Frances Blum ,  Nancy Dou ,  Matthew J. Glickman ,  Boxin Jiang ,  Orestis Kostakis ,  Justin Langseth ,  Michael Earle Rainey ,  Haoran Yu

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The subject technology receives first party training data provided by an end-user of a baseline machine learning model. The subject technology determines a first set of common features based on the first party training data. The subject technology receives, from at least one data source. The subject technology determines a second set of common features based on the set of datasets. The subject technology trains, using the first set of common features and the second set of common features, a second machine learning model, the second machine learning model incorporating additional training data from the external data supplier during training compared to the baseline machine learning model. The subject technology generates a boosted machine learning model based at least in part on the training, the boosted machine learning model comprising the trained second machine learning model.

公开(公告)号：US11868326B2

公开(公告)日：2024-01-09

申请号：US18074830

申请日：2022-12-05

Applicant: SNOWFLAKE INC.

Inventor： Boxin Jiang ,  Qiming Jiang

IPC: G06F16/00 ,  G06F16/21 ,  G06F16/2458 ,  G06F16/242 ,  G06N3/08

CPC classification number: G06F16/217 ,  G06F16/2433 ,  G06F16/2474 ,  G06N3/08

Abstract: An example method of tuning a machine learning operation can include receiving a data query comprising a reference to an input data set of a database, generating a plurality of unique sets of hyperparameters by varying a hyperparameter value of each set of hyperparameters of the plurality of unique sets of hyperparameters based on the input data set, in response to receiving the data query, training a plurality of machine learning models using the input data set of the data query, each of the plurality of machine learning models configured according to a respective one of a plurality of unique sets of hyperparameters, selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model, and returning the output of the first machine learning model in response to the data query.