Emc IP Holding Company LLC

Otniel Van-Handel

Anat Parush Tzur

Assaf Natanzon

Nimrod Milo

Nir Goldschmidt

Arik Sapojnik

Amihai Savir

Raphael Cohen

Eslam ElNakib

Lina Al Farouk

An apparatus comprises a plurality of storage tiers, at least one data mover module, and skew predictor. A model generator processes information characterizing input-output activity involving one or more of the storage tiers in order to obtain skew measurements indicating portions of the input-output activity directed to portions of the one or more storage tiers for respective periods of time less than at least one corresponding time granularity, and generates a predictive model from the skew measurements. The skew predictor is configured in accordance with the predictive model to convert additional skew measurements obtained for a given period of time less than a desired time granularity to corresponding skew measurements in the desired time granularity. One or more of the converted skew measurements are utilized by the data mover module in controlling transfer of data between the storage tiers. The model generator is part of a machine learning system.

Storage system with machine learning based skew prediction

An apparatus comprises a plurality of storage tiers, at least one data mover module, and a machine learning system coupled to the data mover module. The machine learning system comprises a model generator and a skew predictor. The model generator processes information characterizing input-output activity involving one or more of the storage tiers in order to obtain skew measurements in different time granularities, with the skew measurements indicating portions of the input-output activity directed to portions of the storage tier(s), and generates a predictive model from the skew measurements. The skew predictor is configured in accordance with the predictive model to convert skew measurements in one of the time granularities to corresponding skew measurements in another one of the time granularities. One or more of the converted skew measurements are utilized by the data mover module in controlling transfer of data between the storage tiers.

An apparatus comprises a processing platform configured to implement an expert recommender engine. The expert recommender engine receives information relating to a communication from a user device, and identifies at least one subject matter expert for the communication based on the received information and unstructured text data of a service events database. The expert recommender engine is associated with a clustering module that separates the unstructured text data into topic clusters. The expert recommender engine comprises a collaborative filtering module that receives the topic clusters from the clustering module and utilizes the topic clusters to identify the subject matter expert. The user device is connected with an expert device corresponding to the identified subject matter expert. The expert recommender engine may utilize structured data, social media data and customer satisfaction survey data in combination with the received information and the topic clusters to identify the subject matter expert.

Expert recommendation leveraging topic clusters derived from unstructured text data

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Modiin Elite, Israel

Otniel Van-Handel