Emc IP Holding Company LLC

Emc Corporation

Percy E Rivera Salas

Diego Salomone Bruno

Tiago Salviano Calmon

Angelo E M Ciarlini

Rodrigo Dias Arruda Senra

Rômulo Teixeira De Abreu Pinho

Jonas Furtado Dias

Karin Breitman

Victor Bursztyn

Fabio A M Porto

Fábio A Perosi

Caio Souza

Noel Moreno Lemus

Relevance decay techniques are provided for time-based evaluation of machine learning applications and other classifiers. An exemplary method comprises obtaining time series measurement data; generating an input dataset comprising a plurality of records, wherein each record comprises features extracted from the time series measurement data, a target class corresponding to an event to be identified, and a time lag indicating a difference in time between a given extraction and the event to be identified; evaluating a plurality of classifiers during an evaluation phase using a portion of the input dataset and one or more predefined evaluation metrics weighted using a time-based relevance decay function based on the time lag; and selecting one or more of the classifiers to perform classification of the time series measurement data based on the predefined weighted evaluation metrics during a classification phase. The time lags indicate, for example, a time difference between classification moments of the plurality of classifiers and a respective instance of the event to be identified.

Relevance decay for time-based evaluation of machine learning applications

Traffic condition forecasting techniques are provided that use matrix compression and deep neural networks. An illustrative method comprises obtaining a compressed origination-destination matrix indicating a cost to travel between pairs of a plurality of nodes, wherein the compressed origination-destination matrix is compressed using a locality-aware compression technique that maintains only non-empty data; obtaining a trained deep neural network trained using the compressed origination-destination matrix and past observations of traffic conditions at various times; and applying traffic conditions between two nodes in the compressed origination-destination matrix at a time, t, to the trained deep neural network to obtain predicted traffic conditions between the two nodes at a time, t+&#x394;. A tensor can be generated indicating an evolution of traffic conditions over a time span using a stacked Origination-Destination matrix comprising a plurality of past observations representing the time span.

Traffic condition forecasting using matrix compression and deep neural networks

Methods and apparatus are provided for pattern-aware transformation of time series data to multi-dimensional data for Deep Learning analysis. An exemplary method comprises: obtaining time series data and an indication of seasonal components in the time series data; obtaining the time series data separated into data chunks of a predefined length based on at least one seasonal component; aligning the data chunks based on the at least one seasonal component; generating an image and/or a multi-dimensional vector using the aligned data chunks; and applying the image and/or the multi-dimensional vector to a Deep Learning module to obtain a prediction, a classification and/or a profiling of parameters associated with the time series data. The classification of the parameters comprises, for example, an anomaly detection. The profiling of the parameters comprises, for example, a clustering of the parameters and/or a behavior identification.

Pattern-aware transformation of time series data to multi-dimensional data for deep learning analysis

Methods and apparatus are provided for real-time ad hoc querying of data records, such as call detail records. An exemplary method comprises obtaining data records, wherein each data records comprises a plurality of fields and wherein one or more fields are used to aggregate a history of the data records; storing the data records in a consolidation queue (and optionally, a waiting queue), based on predefined indexing criteria; storing the data records from the consolidation queues in a historical database based on one or more predefined criteria related to a queue length and/or a time interval; creating a pre-processed index that maps indexed information for a given entity to a portion of the historical database; and, in response to a user query regarding attributes of the given entity: retrieving the data records related to the given entity from the historical database based on the pre-processed index for the given entity; storing the retrieved data records of the given entity into an in-memory database; and processing the retrieved data records of the given entity to answer the one or more user queries.

Real-time ad hoc querying of data records

Methods and apparatus are provided for integrating heterogeneous multi-channel data using ontologies. An exemplary method for integrating multi-channel heterogeneous data comprises obtaining a domain-specific mediator ontology; identifying a plurality of target channels; identifying entities pertinent to each of the plurality of channels; describing the entities pertinent to each of the plurality of channels using an ontology description language to generate a plurality of channel specific ontologies; aligning the channel specific ontologies with the domain-specific mediator ontology to generate aligned channel specific and domain-specific mediator ontologies; extracting a plurality of user identities from the plurality of channels; aligning the plurality of extracted user identities across the plurality of channels to link common user identities; generating at least one user profile for at least one of the aligned user identities; and correlating at least one user profile with the aligned channel specific and domain-specific mediator ontologies to generate an omni-channel ontology that integrates the multi-channel heterogeneous data.

Integration of heterogenous data using omni-channel ontologies

Methods and apparatus are provided for automatically identifying possible faults in large seismic datasets. An exemplary method comprises obtaining the seismic data; calculating a coherence cube of the seismic data; performing the following steps for a plurality of two-dimensional seismic sections of the coherence cube: (i) applying a threshold to the coherence cube to obtain a binary image representation comprising continuities; (ii) identifying edges of continuity areas in the binary image representation to identify changes in the continuities as fault point candidates; (iii) identifying fault points in the obtained seismic data based on a fault confidence value indicating a likelihood that a given point is part of a fault; (iv) creating one or more fault segments from the identified fault points; and (v) joining fault segments into fault lines using geological and/or geometrical constraints; and generating three-dimensional fault surfaces from the fault lines in the plurality of two-dimensional seismic sections. The exemplary automatic fault detection method can be parallelized.

Methods and apparatus for automatic identification of faults on noisy seismic data

Methods and apparatus are provided for analytical processing of provenance data for High Performance Computing workflow optimization. Prediction models for a workflow composed of a plurality of activities are created by (i) generating a plurality of prediction functions from input features and output features of the workflow, wherein each of the prediction functions predicts at least one output feature of at least one of activities of the workflow based on the input features of at least one activity; and (ii) combining the plurality of prediction functions to generate the prediction models, wherein each of the prediction models predicts a final output feature of the workflow based on an input of the workflow for a given execution plan of the workflow. A plurality of the prediction models can be evaluated to select, among the possible execution plans, an instantiation of the workflow for a given input that optimizes a given user goal.

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Rio de Janeiro, Brazil

Percy E Rivera Salas