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公开(公告)号：US11243764B1
公开(公告)日：2022-02-08
申请号：US16948259
申请日：2020-09-10
发明人： Yi Shao , Liang Wang , Lei Tian , Zhe ZL Liu , Chun Lei Xu
摘要： According to embodiments of the present disclosure, a method, a device and a computer program product for code deployment are proposed. In the method, a deployment strategy for deploying code into a plurality of computing environments and respective amounts of resources provided by the plurality of computing environments are obtained. At least one code segment of the code to be deployed in a corresponding computing environment comprised in the plurality of computing environments is determined based on the deployment strategy and the respective amounts of resources. An amount of resources provided by the corresponding computing environment is sufficient to run the at least one code segment. The at least one code segment is deployed into the corresponding computing environment.
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公开(公告)号：US11966822B2
公开(公告)日：2024-04-23
申请号：US17035699
申请日：2020-09-29
发明人： Chun Lei Xu , Si Er Han , Shi Bin Liu , Yi Shao , Lei Tian , Hao Zheng , Jia Rui Wang
IPC分类号： G06F18/22 , G06F18/213 , G06N20/00 , G06V10/77
CPC分类号： G06N20/00 , G06F18/213 , G06F18/22 , G06V10/7715
摘要： Disclosed are a computer-implemented method, a system and a computer program product for feature processing. In the computer-implemented method for feature processing, two input features selected from multiple features of each sample in a sample set are projected to one resulting feature by one or more processing units based on a specified curve. The sample set is updated by replacing the two input features with the one resulting feature for each sample in the sample set by one or more processing units. The projecting and the updating for the sample set are repeated by one or more processing units until the number of features of each sample in the sample set reaches a predetermined criterion.
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公开(公告)号：US20230316151A1
公开(公告)日：2023-10-05
申请号：US17709704
申请日：2022-03-31
发明人： Lei Tian , Han Zhang , Yi Shao , Dong Hai Yu , Chun Lei Xu , Xiao Ling Yang
IPC分类号： G06N20/20
CPC分类号： G06N20/20
摘要： Constructing a feature segment-based ensemble can include generating a data structure for each element of an initial set of training data. Multiple strongly correlated features of the elements can be identified as well as weakly correlated features. For each strongly correlated feature, a feature segmentation training set can be generated, each training set's elements each containing one of the strongly correlated features and excluding other strongly correlated features. One or more machine learning algorithms can be selected from a software library. The one or more machine learning algorithms can be applied to the feature segmentation training sets to train multiple machine learning models. Each machine learning model that improves the predictive accuracy of the feature segment-based ensemble can be integrated in the feature segment-based ensemble.
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公开(公告)号：US20230011835A1
公开(公告)日：2023-01-12
申请号：US17370071
申请日：2021-07-08
发明人： Ning Zhang , Yi Shao , Jing Xu , Xue Ying Zhang , Na Zhao
摘要： An approach is provided in which the approach trains a first machine learning model using a set of features corresponding to a set of build blocks. The set of build blocks include at least one dependency build block and at least one artifact package build block. The approach predicts a set of risk values of the set of build blocks using the trained first machine learning model, and marks at least one of the build blocks as a bottleneck in response to comparing the set of risk values against a risk threshold.
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公开(公告)号：US10558919B2
公开(公告)日：2020-02-11
申请号：US15257007
申请日：2016-09-06
IPC分类号： G06N5/02
摘要： An approach to optimizing predictive model analysis, comprising creating one or more model templates, decomposing a predictive model, wherein model information is extracted from the predictive model, storing the model information in the one or more model templates, creating a plurality of sub-models, associated with the predictive model, using the stored model information, sending the plurality of sub-models to a scoring engine, receiving results based on the plurality of sub-models from the scoring engine and generating predictions based on combining the results received from the scoring engine. The generated predictions can be sent to one or more analytic applications for further processing.
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公开(公告)号：US20190065979A1
公开(公告)日：2019-02-28
申请号：US15692963
申请日：2017-08-31
发明人： Yi Shao , Liang Wang , Jing Xu , Jing James Xu
摘要： According to an embodiment, a method, computer system, and computer program product for managing data is provided. The present invention may include accumulating a plurality of predicted outputs according to a data accumulation rule. The plurality of predicted outputs is generated by a predictive model executed by a first system. The present invention may include evaluating, by a second system, an accuracy of the predictive model. Evaluating the accuracy of the predictive model may include determining a degree of difference between the plurality of predicted outputs and information generated during a development stage of the predictive model. The present invention may include determining whether the accuracy of the predictive model has declined by an amount which exceeds a pre-determined threshold. The present invention may include updating the predictive model.
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公开(公告)号：US11847539B2
公开(公告)日：2023-12-19
申请号：US17370071
申请日：2021-07-08
发明人： Ning Zhang , Yi Shao , Jing Xu , Xue Ying Zhang , Na Zhao
摘要： An approach is provided in which the approach trains a first machine learning model using a set of features corresponding to a set of build blocks. The set of build blocks include at least one dependency build block and at least one artifact package build block. The approach predicts a set of risk values of the set of build blocks using the trained first machine learning model, and marks at least one of the build blocks as a bottleneck in response to comparing the set of risk values against a risk threshold.
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公开(公告)号：US10949764B2
公开(公告)日：2021-03-16
申请号：US15692963
申请日：2017-08-31
发明人： Yi Shao , Liang Wang , Jing Xu , Jing James Xu
摘要： According to an embodiment, a method, computer system, and computer program product for managing data is provided. The present invention may include accumulating a plurality of predicted outputs according to a data accumulation rule. The plurality of predicted outputs is generated by a predictive model executed by a first system. The present invention may include evaluating, by a second system, an accuracy of the predictive model. Evaluating the accuracy of the predictive model may include determining a degree of difference between the plurality of predicted outputs and information generated during a development stage of the predictive model. The present invention may include determining whether the accuracy of the predictive model has declined by an amount which exceeds a pre-determined threshold. The present invention may include updating the predictive model.
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公开(公告)号：US20240086464A1
公开(公告)日：2024-03-14
申请号：US17930461
申请日：2022-09-08
CPC分类号： G06F16/81 , G06F16/258 , G06F16/86
摘要： A computer-implemented technique for decomposing semi-structured data is provided. In this technique, metadata for a predetermined number of records can be collected from semi-structured data that includes several records. A structured format is generated based on the metadata and the plurality of records is decomposed with the structured format.
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公开(公告)号：US20220101183A1
公开(公告)日：2022-03-31
申请号：US17035699
申请日：2020-09-29
发明人： Chun Lei Xu , Si Er Han , Shi Bin Liu , Yi Shao , Lei Tian , Hao Zheng , Jia Rui Wang
摘要： Disclosed are a computer-implemented method, a system and a computer program product for feature processing. In the computer-implemented method for feature processing, two input features selected from multiple features of each sample in a sample set are projected to one resulting feature by one or more processing units based on a specified curve. The sample set is updated by replacing the two input features with the one resulting feature for each sample in the sample set by one or more processing units. The projecting and the updating for the sample set are repeated by one or more processing units until the number of features of each sample in the sample set reaches a predetermined criterion.
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