Emc IP Holding Company LLC

Nimrod Milo

Omer Sagi

Amihai Savir

Assaf Natanzon

Anat Parush Tzur

Oshry Ben-Harush

Alon Grubshtein

Nir Goldschmidt

Arik Sapojnik

Otniel Van-Handel

Haim Halbfinger

Gilad Braunschvig

Ronen Halsadi

Jeroen Zonneveld

Danny Croitoru

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system by monitoring and recording all of the interactions of users interacting with data assets in the FBDL system, providing all of the user interactions to a recommendation engine, calculating relevance of information in the FBDL system to each user, and recommending one or more new data assets to a target user based on the relevance of the information. The relevance comprises the target user's past interactions with the data assets based and the cumulative interactions of other users with the data assets, such that if one or more of the other users has similar interaction behavior to the target user, then knowledge of the one or more other users can impact the relevance of the information with regard to the one or more new data assets suggested to the target user.

Recommendation system for data assets in federation business data lake environments

A machine learning-based recommendation system is provided for root cause analysis of service issues. An illustrative method of a machine learning system comprises extracting features from service issue investigation log sets corresponding to previously considered service issues; generating representations for the service issue investigation log sets based on the corresponding extracted features; and storing the representations in a knowledge base. In conjunction with obtaining an additional service issue investigation log set, the illustrative method generates a representation of the additional service issue investigation log set; identifies representations previously stored in the knowledge base as candidate service issues related to the additional service issue based on pairwise probabilities indicating whether the additional service issue is related to at least a subset of the previously considered service issues; and presents information characterizing the service issue investigation log sets corresponding to respective ones of the identified representations.

Machine learning-based recommendation system for root cause analysis of service issues

Application data is received from a plurality of monitored applications. The application data is parsed into a plurality of features describing an operation of the plurality of monitored applications. A counter associated with at least one of the plurality of features is incremented. A system health is derived for the plurality of monitored applications from the counter.

System operational analytics

Methods and apparatus are provided for multi-site time series data analysis. An exemplary method comprises obtaining a plurality of vectors over a period of time, wherein each vector comprises a local health score from each of a plurality of distributed sites that perform operational analytics on time-series data; determining a distribution of the vectors during a training phase; and calculating a global health score for the plurality of distributed sites for a given vector during a prediction phase based on a distance of the given vector from a center of mass of the distribution. Feedback is optionally provided to individual sites, such as an indication of a local health score of an individual site relative to the global health score, and adjustments to thresholds and/or parameters used by the individual site.

Methods and apparatus for multi-site time series data analysis

A method in one embodiment comprises extracting features from each of a plurality of software investigation log sets, generating representations for respective ones of the software investigation log sets based at least in part on the corresponding extracted features, and storing the representations in a knowledge base. In conjunction with obtaining at least one additional software investigation log set, the method generates a representation of the additional software investigation log set, identifies one or more of the representations previously stored in the knowledge base that exhibit at least a specified similarity to the representation of the additional software investigation log set in accordance with one or more statistical models, and presents information characterizing the one or more software investigation log sets corresponding to respective ones of the identified one or more representations in a user interface. The method is illustratively implemented in a machine learning system of a processing platform.

Similarity-based retrieval of software investigation log sets for accelerated software deployment

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.

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Gedera, Israel

Nimrod Milo