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    Systems and methods of optimizing resource allocation using machine learning and predictive control
    2.
    发明授权
    Systems and methods of optimizing resource allocation using machine learning and predictive control 有权

    公开(公告)号：US11922217B2

    公开(公告)日：2024-03-05

    申请号：US17097178

    申请日：2020-11-13

    申请人： Nasdaq, Inc.

    发明人： Shihui Chen ,  Keon Shik Kim ,  Douglas Hamilton

    IPC分类号： G06F9/50 ,  G06N20/00

    CPC分类号： G06F9/5027 ,  G06F9/50 ,  G06N20/00

    摘要： A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period. Predictive machine learning model(s) and decision-making machine learning model(s) are adjusted based on the performance metrics to improve respective performance(s).

    Dynamically selecting among learned and non-learned indexes for data access
    4.
    发明授权
    Dynamically selecting among learned and non-learned indexes for data access 有权

    公开(公告)号：US11726974B2

    公开(公告)日：2023-08-15

    申请号：US17727886

    申请日：2022-04-25

    申请人： Nasdaq, Inc.

    发明人： Jonathan Rivers ,  Douglas Hamilton ,  Leonid Rosenfeld

    IPC分类号： G06F16/22 ,  G06N3/08 ,  G06F16/248 ,  G06F16/28

    CPC分类号： G06F16/2272 ,  G06F16/248 ,  G06F16/285 ,  G06N3/08

    摘要： The described technology relates to systems and techniques for accessing a database by dynamically choosing an index from a plurality of indexes that includes at least one learned index and at least one non-learned index. The availability of learned and non-learned indexes for accessing the same database provides for flexibility in accessing the database, and the dynamic selection between learned indexes and non-learned indexes provide for choosing the index based on the underlying data in the database and the characteristics of the query. Certain example embodiments provide a learned model that accepts a set of features associated with the query as input, and outputs a set of evaluated weights for respective features, which are then processed according to a set of rules to predict the most efficient index to be used.

    Dynamically selecting among learned and non-learned indexes for data access
    5.
    发明授权
    Dynamically selecting among learned and non-learned indexes for data access 有权

    公开(公告)号：US11321296B1

    公开(公告)日：2022-05-03

    申请号：US17154855

    申请日：2021-01-21

    申请人： Nasdaq, Inc.

    发明人： Jonathan Rivers ,  Douglas Hamilton ,  Leonid Rosenfeld

    IPC分类号： G06F16/22 ,  G06N3/08 ,  G06F16/248 ,  G06F16/28

    摘要： The described technology relates to systems and techniques for accessing a database by dynamically choosing an index from a plurality of indexes that includes at least one learned index and at least one non-learned index. The availability of learned and non-learned indexes for accessing the same database provides for flexibility in accessing the database, and the dynamic selection between learned indexes and non-learned indexes provide for choosing the index based on the underlying data in the database and the characteristics of the query. Certain example embodiments provide a learned model that accepts a set of features associated with the query as input, and outputs a set of evaluated weights for respective features, which are then processed according to a set of rules to predict the most efficient index to be used.

    Systems and methods of processing diverse data sets with a neural network to generate synthesized data sets for predicting a target metric
    9.
    发明授权
    Systems and methods of processing diverse data sets with a neural network to generate synthesized data sets for predicting a target metric 有权

    公开(公告)号：US11361251B2

    公开(公告)日：2022-06-14

    申请号：US16744236

    申请日：2020-01-16

    申请人： Nasdaq, Inc.

    发明人： Douglas Hamilton

    IPC分类号： G06N20/00 ,  G06N3/08 ,  G06N3/063 ,  G06N3/04

    摘要： A computer system receives and stores data sets, a target metric, and a parameter that indicates a desired number of synthesized data sets. A hardware processor performs operations where each processing node of a neural network weights input data set values, determines gating operations to select processing operations, and generates a node output by applying the gating operations to weighted input data set values. The neural network is trained by modifying the gating operations, the input weight values, and the node output weight value until convergence. One or more nodes is selected having a larger magnitude node output weight value. Selected input data set values are processed with selected processing nodes using a selected subset of gating operations to produce the desired number of synthesized data sets. Names are generated for each of the synthesized data sets.