Emc Corporation

Emc IP Holding Company LLC

Monica Barros

Jonas Furtado Dias

Angelo E M Ciarlini

Vinícius Michel Gottin

André De Almeida Maximo

Adriana Bechara Prado

Diego Salomone Bruno

Mauricio Melo Camara

Thiago De Sa Feital

André Maximo

José Carlos Costa Da Silva Pinto

Rafael Marinho Soares

Methods and apparatus are provided for process monitoring based on large-scale combinations of time series data. An exemplary method comprises generating a model from time series data for a given target time series; determining whether a first difference between measured values and predicted values based on the model exceeds a predefined threshold indicating a target prediction error; in response to a detected target prediction error, performing evaluations of (i) a neighborhood coherence comprising an average of variables of the model weighted by corresponding coefficients on a predefined neighborhood time window, and/or (ii) a second difference between a given value of at least one variable in the model and an average value of the at least one variable based on a training dataset; providing notifications when first predefined criteria based on the evaluations are satisfied; and updating the model when second predefined criteria based on the evaluations are satisfied.

Process monitoring based on large-scale combination of time series data

Methods and apparatus are provided for domain-tailored detection of outliers, patterns, and/or events in data streams. An exemplary method comprises obtaining a domain-dependent definition of (i) data outliers based on predefined outlier criteria; (ii) data patterns based on predefined pattern criteria; and/or (iii) data events based on predefined event criteria; obtaining time series measurement data from a plurality of sensors; determining, substantially simultaneously with the obtaining, whether individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events; and storing the individual samples with an indication of whether the individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events. The domain-dependent definitions are optionally specified using a declarative command language. Query are optionally processed comprising one or more declarative statements that reference and/or manipulate the data outliers, data patterns and/or data events.

Database system and methods for domain-tailored detection of outliers, patterns, and events in data streams

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.

Massively parallel processing (MPP) large-scale combination of time series data

Growing community of inventors

Rio de Janeiro, Brazil

Monica Barros