Dell Products Inc.

Romulo Teixeira De Abreu Pinho

Vinicius Michel Gottin

Paulo Abelha Ferreira

Pablo Nascimento Da Silva

Elie Antoun Jreij

Amihai Savir

Fagner Scheibel Reinaldo

Aline Rodrigues Da Cruz

Paulo Acassio Martins Oliveira

Aviv Zeilig

Peter Ziesmann

Roberto Stelling

Rachel Shalom

Norton Zamboni Junior

A system, method, and computer-readable medium for performing a data center management and monitoring operation to predict event failures, comprising taking time stamped telemetry metrics of data asset clusters; sampling the time stamped telemetry metrics; separating the sampled time stamped telemetry metrics into groups based on metric functionality; providing data of the groups into a residual neural network to learn patterns of the three groups; and concatenating output of the residual neural network of the groups, wherein concatenated output is feed back to the residual neural network to learn relationships of the three groups.

Data center monitoring and management operation for data unavailable/data loss (DU/DL) predictions, including service level agreement (SLA) failure prediction

Techniques are provided for customer service ticket similarity determination using an updated encoding model based on similarity feedback from a user. One method comprises obtaining encodings of customer service tickets in a vector space using an encoding model; determining pairwise similarities for the encodings of the customer service tickets; obtaining feedback from a user regarding the pairwise similarities for a subset of the encodings; updating pairwise similarities for the subset of the encodings using the feedback from the user; generating an updated encoding model by processing the updated pairwise similarities for the subset of the encodings of the customer service tickets using a supervised learning algorithm; and processing at least one customer service ticket based at least in part on the updated encoding model. The feedback from the user may indicate a similarity of two or more of the plurality of customer service tickets.

Customer service ticket similarity determination using updated encoding model based on similarity feedback from user

Techniques are disclosed for predicting a table column using machine learning. For example, a system can include at least one processing device including a processor coupled to a memory, the processing device being configured to implement the following: determining a local word density for words in a table, the local word density measuring a count of other words in a first region surrounding the words; determining a local numeric density for the words, the local numeric density measuring a proportion of digits in a second region surrounding the words; determining semantic associations for the words by processing the words using an ML-based semantic association model trained based on surrounding words in nearby table columns and rows; and predicting a table column index for the words by processing the table using an ML-based table column model trained based on the local word density, local numeric density, and semantic association.

Table column identification using machine learning

Techniques for table row identification using machine learning are disclosed herein. For example, a method can include detecting a table body in a document by processing the document using a machine learning (ML)-based table body model; predicting an initial table row index for one or more words among a plurality of words obtained in the document, wherein the one or more words are determined to be within the table body; and determining a table row index for the one or more words using an ML-based table row model that is trained based on the predicted initial table row index for the one or more words.

Table row identification using machine learning

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Niterói, Brazil

Romulo Teixeira De Abreu Pinho