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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
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.
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公开(公告)号：US11880364B2
公开(公告)日：2024-01-23
申请号：US17157233
申请日：2021-01-25
Applicant: Snowflake Inc.
Inventor： Qiming Jiang , Orestis Kostakis
IPC: G06F16/2453 , G06F16/2455 , G06N20/00
CPC classification number: G06F16/24542 , G06F16/2455 , G06N20/00
Abstract: The subject technology receives a query directed to a set of source tables, each source table organized into a set of micro-partitions. The subject technology determines a set of metadata, the set of metadata comprising table metadata, query metadata, and historical data related to the query. The subject technology predicts, using a machine learning model, an indicator of an amount of computing resources for executing the query based at least in part on the set of metadata. The subject technology generates a query plan for executing the query based at least in part on the predicted indicator of the amount of computing resources. The subject technology executes the query based at least in part on the query plan.
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公开(公告)号：US20230401185A1
公开(公告)日：2023-12-14
申请号：US18112934
申请日：2023-02-22
Applicant: Snowflake Inc.
Inventor： Orestis Kostakis , Prasanna V. Krishnan , Subramanian Muralidhar , Shakhina Pulatova , Megan Marie Schoendorf
IPC: G06F16/215 , G06F16/2457 , G06F16/25 , G06F16/176
CPC classification number: G06F16/215 , G06F16/24578 , G06F16/256 , G06F16/176
Abstract: A set of affinity metrics may be determined for a set of listings, each listing of the set of listings comprising data to be shared through a data exchange, wherein the set of affinity metrics includes a set of characteristics allowing identification of a listing having one or more characteristics in the set of characteristics. For each pair of listings of the set of listings, an affinity score can be calculated, using the set of affinity metrics, and stored as part of the record in an affinity store. One or more listings of the set of listings using the affinity score between the first listing of the set of listings and the one or more listings of the set of listings can be presented.
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公开(公告)号：US20230393816A1
公开(公告)日：2023-12-07
申请号：US18362114
申请日：2023-07-31
Applicant: Snowflake Inc.
Inventor： Jianzhun Du , Orestis Kostakis , Kristopher Wagner , Yijun Xie
Abstract: The subject technology identifies a set of functions included in a set of files corresponding to a library. The subject technology, for each function in the set of functions, registers the function as a user defined function (UDF). The subject technology generates a name for the function based at least in part on a predetermined prefix, wherein the predetermined prefix comprises an alphanumeric string. The subject technology generates, using at least a particular set of input parameters utilized by the function and a particular type of parameter of each input parameter of the particular set of input parameters, a particular set of source code. The subject technology stores information corresponding to the function in a metadata database. The subject technology provides access to the function in a different application.
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公开(公告)号：US20230385284A1
公开(公告)日：2023-11-30
申请号：US17804434
申请日：2022-05-27
Applicant: Snowflake Inc.
Inventor： Matthew J. Glickman , Orestis Kostakis , Justin Langseth
IPC: G06F16/2455 , G06F16/242
CPC classification number: G06F16/24568 , G06F16/2456 , G06F16/244 , G06F16/24564
Abstract: Systems, methods, and machine-readable storage devices provide for identifying a user dataset on a distributed database. The system includes generating a similarity score dataset that indicates a similarity between the user dataset and a plurality of datasets of other users of the distributed database. The system generates a plurality of overlap queries that are configured to output overlap datasets between the user dataset and one or more of the plurality of datasets. The system further generates a results dataset by applying one or more of the plurality of overlap queries to a joined dataset comprising data from the user dataset and one of the plurality of datasets of other users on the distributed database.
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Patent Agency Ranking
公开(公告)号：US12130811B2
公开(公告)日：2024-10-29
申请号：US18362869
申请日：2023-07-31
Applicant: Snowflake Inc.
Inventor： Qiming Jiang , Orestis Kostakis , John Reumann
IPC: G06F16/00 , G06F16/2453 , G06F16/27
CPC classification number: G06F16/24542 , G06F16/27
Abstract: A system for improving task scheduling on a cloud data platform is provided. A task to be executed using resources of a computing cluster is received. A task execution plan is generated and information about data to be used for the ask is accessed. Resource requirements for executing the task are predicted by applying machine learning to the task execution plan and the information about the data. Assignment data is generated to execute the task on the resources by applying machine learning information about a current state of the resources and predicted resource requirements.
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公开(公告)号：US20240273417A1
公开(公告)日：2024-08-15
申请号：US18643787
申请日：2024-04-23
Applicant: Snowflake Inc.
Inventor： Orestis Kostakis , Justin Langseth
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: Embodiments of the present disclosure may provide a data sharing system implemented as a local application in a consumer database of a distributed database. The local application can include a training function and a scoring function to train a machine learning model on provider and consumer data, and generate output data by applying the trained machine learning model on input data. The input data can include data portions from a consumer database and a provider database that are joined to create a joined dataset for scoring.
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公开(公告)号：US12050890B2
公开(公告)日：2024-07-30
申请号：US18362114
申请日：2023-07-31
Applicant: Snowflake Inc.
Inventor： Jianzhun Du , Orestis Kostakis , Kristopher Wagner , Yijun Xie
Abstract: The subject technology identifies a set of functions included in a set of files corresponding to a library. The subject technology, for each function in the set of functions, registers the function as a user defined function (UDF). The subject technology generates a name for the function based at least in part on a predetermined prefix, wherein the predetermined prefix comprises an alphanumeric string. The subject technology generates, using at least a particular set of input parameters utilized by the function and a particular type of parameter of each input parameter of the particular set of input parameters, a particular set of source code. The subject technology stores information corresponding to the function in a metadata database. The subject technology provides access to the function in a different application.
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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
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.
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公开(公告)号：US20240119051A1
公开(公告)日：2024-04-11
申请号：US18545889
申请日：2023-12-19
Applicant: Snowflake Inc.
Inventor： Qiming Jiang , Orestis Kostakis
IPC: G06F16/2453 , G06F16/2455 , G06N20/00
CPC classification number: G06F16/24542 , G06F16/2455 , G06N20/00
Abstract: The subject technology receives a query directed to a set of source tables, each source table organized into a set of micro-partitions. The subject technology determines a set of metadata, the set of metadata comprising table metadata, query metadata, and historical data related to the query. The subject technology predicts, using a machine learning model, an indicator of an amount of computing resources for executing the query based at least in part on the set of metadata. The subject technology generates a query plan for executing the query based at least in part on the predicted indicator of the amount of computing resources. The subject technology executes the query based at least in part on the query plan.
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