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公开(公告)号：US20180307731A1
公开(公告)日：2018-10-25
申请号：US15946128
申请日：2018-04-05
Applicant: SAS Institute Inc.
Inventor： Wei Xiao
CPC classification number: G06F17/30516 , G06F17/16 , G06F17/30315 , G06T11/206 , G06T2200/24
Abstract: A data streaming environment provides a summary of streaming data from a sensor that is an Internet of things device. An input interface receives the streaming data. A processor is communicatively coupled to the input interface for processing the streaming data. The processed streaming data includes, but is not limited to, a plurality of records and variables that describe a characteristic of a physical object. A computer-readable medium has instructions stored thereon that, when executed by the processor, cause the processor to execute a correlation update application with the received streaming data to provide a correlation between two variables of the streaming data. The non-transitory computer-readable medium further stores sum and bin data for the correlation update application to compute the correlation. The output interface provides the processed streaming data to be visually presented in one or more data graphs on a display device.
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公开(公告)号：US09830558B1
公开(公告)日：2017-11-28
申请号：US15185277
申请日：2016-06-17
Applicant: SAS Institute Inc.
Inventor： Arin Chaudhuri , Deovrat Vijay Kakde , Maria Jahja , Wei Xiao , Seung Hyun Kong , Hansi Jiang , Sergiy Peredriy
CPC classification number: G06N99/005 , G06F17/30539 , H04L67/02
Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
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公开(公告)号：US10157319B2
公开(公告)日：2018-12-18
申请号：US15894002
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉt is computed using ĉt=Σi=1mst[i]. A probability value for ĉt is computed using p=Σi=ĉt+1m+1Hc[i]/Σi=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ĉt occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
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Patent Agency Ranking
公开(公告)号：US20170323221A1
公开(公告)日：2017-11-09
申请号：US15185277
申请日：2016-06-17
Applicant: SAS Institute Inc.
Inventor： Arin Chaudhuri , Deovrat Vijay Kakde , Maria Jahja , Wei Xiao , Seung Hyun Kong , Hansi Jiang , Sergiy Peredriy
CPC classification number: G06N99/005 , G06F17/30539 , H04L67/02
Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
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公开(公告)号：US10289619B2
公开(公告)日：2019-05-14
申请号：US15946128
申请日：2018-04-05
Applicant: SAS Institute Inc.
Inventor： Wei Xiao
IPC: G06F17/16 , G06F16/2455 , G06F16/22 , G06F17/18 , G06T11/20
Abstract: A data streaming environment provides a summary of streaming data from a sensor that is an Internet of things device. An input interface receives the streaming data. A processor is communicatively coupled to the input interface for processing the streaming data. The processed streaming data includes, but is not limited to, a plurality of records and variables that describe a characteristic of a physical object. A computer-readable medium has instructions stored thereon that, when executed by the processor, cause the processor to execute a correlation update application with the received streaming data to provide a correlation between two variables of the streaming data. The non-transitory computer-readable medium further stores sum and bin data for the correlation update application to compute the correlation. The output interface provides the processed streaming data to be visually presented in one or more data graphs on a display device.
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6.发明申请公开(公告)号：US20180239966A1
公开(公告)日：2018-08-23
申请号：US15893959
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
CPC classification number: G06K9/00771 , G06F9/30036 , G06F17/16 , G06F17/18 , G06K9/481 , G06K9/623 , G06K9/6232 , G06K9/6247 , G06K9/6249 , G06K2009/3291
Abstract: A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector ν for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
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公开(公告)号：US10303954B2
公开(公告)日：2019-05-28
申请号：US15893959
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
Abstract: A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector v for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
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8.发明申请公开(公告)号：US20180239740A1
公开(公告)日：2018-08-23
申请号：US15894002
申请日：2018-02-12
Applicant: SAS Institute Inc.
Inventor： Wei Xiao , Jorge Manuel Gomes da Silva , Saba Emrani , Arin Chaudhuri
CPC classification number: G06K9/00771 , G06F9/30036 , G06F17/16 , G06F17/18 , G06K9/481 , G06K9/623 , G06K9/6232 , G06K9/6247 , G06K9/6249 , G06K2009/3291
Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉt is computed using ĉt=Σi=1mst[i]. A probability value for ĉt is computed using p=Σi=ĉt+1m+1Hc[i]/Σi=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ĉt occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
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