Emc Corporation

Emc IP Holding Company LLC

Dell Products Inc.

Sudhir Vijendra

Patricia G S Florissi

Patricia Florissi

Stephen J Todd

Patricia Gomes Soares Florissi

Ido Singer

Anand Singh

Amanuel Ronen Artzi

Ehud Gudes

Amnon Meisels

Shashidhar Krishnaswamy

Yaron Gonen

Benny Lutati

Qing Ye

Chao-Wei Ou

Igg Adiwijaya

Narayanan Krishnakumar

Ehud Kleers

Pankaj Batra

Hung The Dinh

Yael Villa

Qiyan Chen

Danilo Florissi

William Z Zahavi

Jidong Chen

David Ohsie

Ofri Masad

Satish Ranjan Das

Lida He

Karthikeyan Srinivasan

Jeffrey A Schriesheim

Reddeppa Kollu

Arockiaraj Durairaj

Chetan Pudiyanda Somaiah

Ivan Valentinovich Gumenyuk

Senthil Thiagrajan

Barbara Latulippe

Jonathan Reeve

Vadym Nazarchuk

Pavel B Egorov

Inga Petryaevskaya

Padraig D Murphy

Shareef Muhammad Mahmoud Bassiouny

Calvin Donahue Smith

Sachin Yadav

A distributed ledger is maintained in accordance with an enterprise. The distributed ledger includes a plurality of nodes such that one or more entities internal to the enterprise and one or more entities external to the enterprise each have access to at least one of the plurality of nodes. Electronic agreements (for example, product/service licenses) between at least a portion of the one or more entities internal to the enterprise and at least a portion of the one or more entities external to the enterprise are managed in association with the distributed ledger. Management includes generating and recording transactions associated with the electronic agreements on the distributed ledger to enable the one or more entities internal to the enterprise and the one or more entities external to the enterprise permissioned access to one or more of the recorded transactions.

Electronic agreement data management architecture with blockchain distributed ledger

A processing platform includes a plurality of processing devices each including a processor coupled to a memory, and is configured to communicate over at least one network with one or more data sources. The processing platform is further configured to receive input data from the one or more data sources, to identify an instance of a key performance indicator (KPI) management data model associated with the received input data, and to perform a particular KPI service on the received input data in accordance with the identified instance of the KPI management data model. Performing the particular KPI service illustratively includes: utilizing the identified instance of the KPI management data model to extract one or more metrics from the received input data and to compute one or more KPIs based at least in part on the one or more extracted metrics; and generating analytics output including the one or more computed KPIs for the received input data.

Model-based key performance indicator service for data analytics processing platforms

At least one application data set stored in a data repository is obtained. The application data set is analyzed to generate at least one metadata node. The at least one metadata node is combined with at least one other related node to form a hierarchical data structure. One or more valuation algorithms are executed against the hierarchical data structure to calculate a value for the data set represented in the hierarchical data structure.

Metadata-based data valuation

A method includes identifying container metrics for containers running in a container environment, collecting container data for the containers, and generating an adaptive threshold for a given one of the identified container metrics. The adaptive threshold specifies one or more values for the given container metric for a designated time period. The adaptive threshold is generated utilizing a scoring algorithm that determines a range of accepted container behavior for the designated time period by analyzing the collected container data using one or more machine learning algorithms. The method also includes monitoring behavior of the containers during the designated time period utilizing the adaptive threshold, and generating an alert responsive to detecting that the monitored behavior of a given one of the containers is outside the range of accepted container behavior for the designated time period specified by the given adaptive threshold for the given container metric.

Adaptive thresholds for containers

One or more data assets associated with a data repository of a given enterprise is identified. Each of the one or more data assets is tagged with economic driver metadata that links each of the one or more data assets to at least one economic driver category from a plurality of economic driver categories associated with the given enterprise. At least one economic value is calculated for each of the one or more data assets based on the at least one economic driver category linked to each of the one or more data assets. Calculated economic values for the one or more data assets are stored in a valuation data structure.

Economic valuation of data assets

A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Distributed catalog service for multi-cluster data processing platform

A method includes receiving, at a controller device, a request to initiate a sharing action between at least two visual devices wirelessly paired with the controller device. The method also includes selecting, using at least one user interface feature of the controller device, a source visual device for the sharing action and a destination visual device for the sharing action. The method further includes providing one or more resources from a display of the source visual device to a display of the destination visual device by streaming the resources to the controller device in a first display format supported by the source visual device, converting the resources from the first display format to a second display format supported by the destination device, and streaming the resources in the second display format from the controller device to the destination device.

Controller for providing sharing between visual devices

An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to establish a connection between a given one of a plurality of Internet of Things (IoT) clients and a given one of a plurality of IoT brokers via a connectivity layer providing the plurality of IoT clients with centralized access to the plurality of IoT brokers, and to exchange data between the given one of the plurality of IoT clients and the given one of the plurality of IoT brokers via the connectivity layer.

Internet of Things connectivity platform

A method, apparatus, and computer implemented method for analyzing a Big Data dataset, the method comprising performing analysis on a big data dataset by applying a set of analytical tool to a Big Data Model; wherein the Big Data Model decouples the Big Data dataset into properties and metadata; wherein each of the properties represent part of the Big Data dataset to enable processing and analysis; wherein the metadata enables calculation of summary information for the Big Data dataset.

Analyzing big data

A data governance method comprises the following steps. One or more data elements associated with an enterprise are identified as one or more critical data elements. A data structure is maintained for the one or more critical data elements. For a given critical data element, the data structure stores information that reflects an impact that the given critical data element had on at least one event associated with the enterprise. The method predicts a risk associated with a subsequent impact that the given critical data element may have on at least one subsequent event associated with the enterprise, wherein the risk of the subsequent impact is predicted using at least a portion of the information stored in the data structure.

Data governance with automated risk valuation via impact per event analytics

An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to distribute in-memory computations across at least first and second nodes of respective distinct data processing clusters of a plurality of data processing clusters over at least one network, and to aggregate results of the distributed in-memory computations for delivery to a requesting client device. The data processing clusters are associated with respective distinct data zones, and the first and second nodes of the respective distinct data processing clusters are configured to perform corresponding portions of the distributed in-memory computations utilizing respective ones of first and second in-memory datasets locally accessible within their respective data zones. The in-memory computations in some embodiments illustratively comprise Spark computations, such as Spark Core batch computations. The in-memory datasets in such an arrangement may comprise respective Spark resilient distributed datasets.

Scalable distributed in-memory computation

A computer implemented method, system, and program product for migration of a user's assets across a cloud comprising Service Units, the method comprising grouping Service Unit devices into cells, wherein the service units comprise storage devices, wherein each cell comprises a group of the Service Unit device comprising a common geographic region, grouping the cells into areas, each cell of the cells grouped into an area comprising a common geographic region, determining the location of the user with respect to the cells, determining the area corresponding to the determined cells, determining which cell of the cells contains the Service Unit device that provides the user with the best services; and transferring the user's assets to the Service Unit within the determined cell.

Self adaptive application and information movement in a cloud environment

A method, apparatus and computer program product including determining a set of objects to be represented in a computer model, determining the relationships between the object to be represented in the computer model, creating representations in the computer model of the set of objects and relationships between the objects, the representations including big data architecture representations and role representations, and where analysis may be performed on the representations of the computer model.

Distributed architecture model and management

Growing community of inventors

Cambridge, MA, United States of America

Sudhir Vijendra