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公开(公告)号：US10860680B1
公开(公告)日：2020-12-08
申请号：US15888079
申请日：2018-02-04
摘要： The present invention extends to methods, systems, and computing system program products for dynamic correlation batch calculation for Big Data. Embodiments of the invention include calculating a correlation for a modified computation set based on a group of components calculated for the pre-modified computation set and one or more groups of components calculated for a computation set to be excluded from the pre-modified computation set and a computation set to be included in the pre-modified computation set, where the size of the to-be-included computation set may or may not be equal to the size of the to-be-excluded computation set. When the size of the to-be-excluded computation set is smaller than half the size of the pre-modified computation set, dynamic correlation batch calculation may reduce computations thereby increasing calculation efficiency, saving computation resources, and reducing computing system's power consumption.
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公开(公告)号：US10467326B1
公开(公告)日：2019-11-05
申请号：US14981248
申请日：2015-12-28
申请人： Jizhu Lu
发明人： Jizhu Lu
摘要： The present invention extends to methods, systems, and computing system program products for decrementally calculating simple linear regression coefficients for Big Data or streamed data. Embodiments of the invention include decrementally calculating one or more components of simple linear regression coefficients for a modified computation set based on the one or more components of simple linear regression coefficients calculated for a previous computation set and then calculating the simple linear regression coefficients for the modified computation set based on the decrementally calculated components. Decrementally calculating simple linear regression coefficients avoids visiting all data elements in the modified computation set and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US10275488B1
公开(公告)日：2019-04-30
申请号：US14964539
申请日：2015-12-09
申请人： Jizhu Lu
发明人： Jizhu Lu
摘要： The present invention extends to methods, systems, and computing system program products for incrementally calculating covariance for Big Data or streamed data. Embodiments of the invention include incrementally calculating one or more components of a covariance for two modified computation subsets based on one or more components of the covariance calculated for two previous computation subsets and then calculating covariance based on the incrementally calculated components. Incrementally calculating the components of a covariance avoids visiting all data elements in the modified computation subsets and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US09760539B1
公开(公告)日：2017-09-12
申请号：US14981197
申请日：2015-12-28
申请人： Jizhu Lu
发明人： Jizhu Lu
CPC分类号： G06F17/18
摘要： The present invention extends to methods, systems, and computing device program products for incrementally calculating simple linear regression coefficients for Big Data or streamed data. Embodiments of the invention include incrementally calculating one or more components of simple linear regression coefficients for a modified computation set based on one or more components of simple linear regression coefficients calculated for a previous computation set and then calculating the simple linear regression coefficients for the modified computation set based on the incrementally calculated components. Incrementally calculating simple linear regression coefficients avoids visiting all data elements in the modified computation set and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US10394810B1
公开(公告)日：2019-08-27
申请号：US14980680
申请日：2015-12-28
申请人： Jizhu Lu
发明人： Jizhu Lu
IPC分类号： G06F17/18 , G06F16/2453
摘要： The present invention extends to methods, systems, and computing system program products for iteratively calculating a Z-score for Big Data. Embodiments of the invention include iteratively calculating one or more components of a Z-score for a modified computation subset based on the one or more components of a Z-score calculated for a previous computation subset and then calculating a Z-score for a selected data element in the modified computation subset based on one or more of the iteratively calculated components. Iteratively calculating a Z-score avoids visiting all data elements in the modified computation subset and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US10282445B1
公开(公告)日：2019-05-07
申请号：US14964541
申请日：2015-12-09
申请人： Jizhu Lu
发明人： Jizhu Lu
摘要： The present invention extends to methods, systems, and computing system program products for incrementally calculating kurtosis for Big Data or streamed data in real time by incrementally calculating one or more components of kurtosis. Embodiments of the invention include incrementally calculating one or more components of a kurtosis for a modified computation subset based on the one or more components of the kurtosis calculated for a pre-modified computation subset and then calculating the kurtosis based on the incrementally calculated components. Incrementally calculating kurtosis avoids visiting all data elements in the modified computation subset and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US10248690B1
公开(公告)日：2019-04-02
申请号：US14964376
申请日：2015-12-09
申请人： Jizhu Lu
发明人： Jizhu Lu
摘要： The present invention extends to methods, systems, and computing system program products for decrementally calculating correlation for Big Data or streamed data. Embodiments of the invention include decrementally calculating one or more components of a correlation for two modified computation subsets based on one or more components of the correlation calculated for two previous computation subsets and then calculating the correlation for the modified computation subsets based on the decrementally calculated components. Decrementally calculating the components of a correlation avoids visiting all data elements in the modified computation subsets and performing redundant computations thereby increasing calculation efficiency, saving computation resources, and reducing computing system's power consumption.
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公开(公告)号：US10235415B1
公开(公告)日：2019-03-19
申请号：US14964436
申请日：2015-12-09
申请人： Jizhu Lu
发明人： Jizhu Lu
摘要： The present invention extends to methods, systems, and computing system program products for iteratively calculating variance and/or standard deviation for Big Data. Embodiments of the invention include iteratively calculating one or more components of a variance and/or a standard deviation in a modified computation subset based on iteratively calculated one or more components of the variance and/or the standard deviation calculated for a previous computation subset and then calculating the variance and/or the standard deviation based on the iteratively calculated components. Iteratively calculating the components of variance and/or standard deviation avoids visiting all data elements in the modified computation subset and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US10225308B1
公开(公告)日：2019-03-05
申请号：US14981070
申请日：2015-12-28
申请人： Jizhu Lu
发明人： Jizhu Lu
IPC分类号： H04L29/06
摘要： The present invention extends to methods, systems, and computing system program products for decrementally calculating Z-score for Big Data or streamed data. Embodiments of the invention include decrementally calculating one or more components of a Z-score for a modified computation subset based on one or more components of a Z-score calculated for a pre-modified computation subset and then calculating a Z-score for a selected data element in the modified computation subset based on one or more of the decrementally calculated components. Decrementally calculating Z-score avoids visiting all data elements in the modified computation subset and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.
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公开(公告)号：US10191941B1
公开(公告)日：2019-01-29
申请号：US14964359
申请日：2015-12-09
申请人： Jizhu Lu
发明人： Jizhu Lu
摘要： The present invention extends to methods, systems, and computing system program products for iteratively calculating a skewness for streamed data. Embodiments of the invention include iteratively calculating one or more components of skewness in an adjusted computation window based on the one or more components of the skewness calculated for a previous computation window and then calculating the skewness based on the iteratively calculated components. Iteratively calculating skewness avoids visiting all data elements in the computation window and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system power consumption.
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