The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 02, 2019

Filed:

Mar. 23, 2016
Applicant:

Emc Corporation, Hopkinton, MA (US);

Inventors:

Angelo E. M. Ciarlini, Rio de Janeiro, BR;

Jonas F. Dias, Rio de Janeiro, BR;

André de Almeida Maximo, Rio de Janeiro, BR;

Vinícius Michel Gottin, Rio de Janeiro, BR;

Monica Barros, Rio de Janeiro, BR;

Assignee:

EMC Corporation, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/50 (2006.01); G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
G06F 17/5009 (2013.01); G06F 17/18 (2013.01); G06F 2217/16 (2013.01);
Abstract

Methods and apparatus are provided for performing massively parallel processing (MPP) large-scale combinations of time series data. A given working compute node in a distributed computing environment obtains a given group of time series data of a plurality of groups of time series data; generates a measurement matrix for the given group based on a plurality of selected time series and a plurality of time lags of the selected time series; processes the measurement matrix to generate a first linear model with a predefined number of first independent selected variables; assigns a score to each first independent selected variable; and provides the first independent selected variables and assigned scores to a master compute node that ranks the first independent selected variables for all groups from all working computing nodes according to assigned scores; selects a predefined number of second independent selected variables based on a final rank to create a final group of time series; and processes the final group of time series to generate a final linear model.


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