Emc Corporation

Emc IP Holding Company LLC

Sun Microsystems, Inc.

Other

Netgenesis Corporation

David Stephen Reiner

Nihar Nanda

John Daniel Hushon

John D Hushon, Jr

Stephen J Todd

David I Dietrich

Timothy Bruce

Thomas J McSweeny

Jeffrey M Miller

David C Wheat

John S Harwood

David Lionel Black

Amnon Naamad

Stephen Glen Graham

John T Cardente

Jidong Chen

George M Ericson

Jeffrey Daniel Esposito

Hang Guo

Haji R Aref

Jeffrey Mark Nick

Helen S Raizen

Said Tabet

Feng Guo

Harold Martin Sandstrom

Qiyan Chen

Yaron Dar

Thomas R Maguire

Tianqing Wang

Dong Xiang

Lintao Wan

Milind Arun Bhandarkar

Jagannathan Ramnarayanan

Robert J Pellowski

Jurgen Leschner

David Russell Brown

Igor Makhlin

Cornelia R Davis

Kenneth Moore

John Yani Arrasjid

Ming Tan

Leonid Levkovich-Maslyuk

Paul Krneta

Edgar StPierre

Harrison Roberts

Rajesh Manjrekar

Theodore Bardasz

Mark Venguerov

Panagiotis Ventikos

Eric Richard

Ron Tozzie

Andrey Abramov

Philip Edwards

An apparatus comprises a processing platform implementing a data set discovery engine and a data set valuation engine. The data set discovery engine is configured to generate data set similarity measures each relating a corresponding one of a plurality of data sets to one or more other ones of the plurality of data sets. The data set valuation engine is coupled to the data set discovery engine and configured to generate valuation measures for respective ones of at least a subset of the plurality of data sets based at least in part on respective ones of the data set similarity measures generated by the data set discovery engine. For example, the data set valuation engine may generate the valuation measure for a given data set as a function of valuation measures previously generated for respective other data sets determined to exhibit at least a threshold similarity to the given data set.

Automated assignment of data set value via semantic matching

An apparatus in one embodiment comprises a processing platform having one or more processing devices. The processing platform is configured to obtain metadata characterizing a plurality of data sets, to generate data set operational signatures for respective ones of at least a subset of the data sets based at least in part on the obtained metadata, and to automatically perform one or more management operations for each of one or more of the data sets based at least in part on respective corresponding ones of the data set operational signatures. The processing platform may be further configured to implement a target signature for a particular data set management policy, and to automatically perform a particular management operation for a given one of the data sets based at least in part on a result of a comparison of the data set operational signature for the given data set to the target signature.

Processing platform configured for data set management utilizing metadata-based data set operational signatures

An apparatus in one embodiment comprises an ingestion manager, a plurality of ingestion engines associated with the ingestion manager, and an analytics platform configured to receive data from the ingestion engines under the control of the ingestion manager. The ingestion manager is configured to interact with one or more of the ingestion engines in conjunction with providing data to a given one of a plurality of analytics workspaces of the analytics platform. For example, the analytics workspaces of the analytics platform are illustratively configured to receive data from respective potentially disjoint subsets of the ingestion engines under the control of the ingestion manager. Additionally or alternatively, the ingestion manager may be configured to implement data-as-a-service functionality for one or more of the analytics workspaces of the analytics platform.

Ingestion manager for analytics platform

An apparatus comprises a processing platform configured for characterization, provisioning and access of data set virtual neighborhoods. The processing platform comprises a virtual neighborhood describer, a virtual neighborhood provisioner and a virtual neighborhood visitor. The virtual neighborhood describer is configured to generate characterizations of data set virtual neighborhoods responsive to one or more requirements, such as specified or derived requirements, or combinations thereof. The virtual neighborhood provisioner is configured to utilize the characterizations to generate provisioned data set virtual neighborhoods responsive to one or more corresponding provisioning requests. The virtual neighborhood visitor is configured to access at least one of: (a) one or more data set content indexes associated with at least one of the provisioned data set virtual neighborhoods, and (b) one or more data sets associated with at least one of the provisioned data set virtual neighborhoods, responsive to a given submitted access request.

Data set virtual neighborhood characterization, provisioning and access

Systems and methods for governing access to or use of assets in an analytics platform. Access to assets is controlled with policies that reference attributes. A context of an access request is defined by collecting attributes associated with an access request. The context is then evaluated in light of attributes referenced by the policy applicable to the asset or with a class of the asset. The access request is granted, denied, or partially granted based on whether the attributes defined by the context of the access request comport with the policy.

Data governance through policies and attributes

A registration framework is disclosed that allows assets to be registered with an analytics platform. When an asset is ingested into the analytics platform, the registration framework operates to register the asset in an asset roster. The asset roster identifies the asset and stores information about the asset. The registration framework can then be used by users to discover assets. Assets may also be deployed by the registration framework to workspaces.

Registration framework for an analytics platform

An apparatus in one embodiment comprises a processing platform comprising one or more processing devices. The processing platform implements a database configured to store metric and level information for each of a plurality of models, and a metric mapper adapted to interface with the database. The metric mapper is configured to utilize the metric and level information stored in the database to establish an association between a first metric characterizing at least one of a workload and a resource in accordance with a first one of the models and a second metric characterizing at least one of a workload and a resource in accordance with a second one of the models. The established association is illustratively applied to relate service requirements specified at least in part using the first metric of the first model to service capabilities specified at least in part using the second metric of the second model.

Processing platform with metric mapping for interoperability of multiple distinct models

An apparatus comprises a processing platform configured for integration of compliance and analytic environments. The compliance and analytic environments are implemented at least in part by the processing platform. The processing platform comprises at least a portion of a data lake at least partially shared between the compliance and analytic environments. The processing platform further comprises one or more integration mechanisms supporting interaction between the compliance and analytic environments via the data lake. Such integration mechanisms may include one or more of providing direct access from the analytic environment to the compliance environment, unified indexing across the compliance and analytics environments, tracking data set interrelationships across the compliance and analytic environments, associating event data of the compliance environment with enterprise activity data, movement of indexing artifacts, movement of contextual data, independent scaling of the compliance and analytic environments, and rehosting of data ingestion from the compliance environment to the analytic environment.

Integrating compliance and analytic environments through data lake cross currents

An apparatus in one embodiment comprises a processing platform implementing an Internet of Things (IoT) distributed management system accessible to a plurality of user devices over at least one network. The processing platform is configured to determine IoT infrastructure for a given IoT deployment in at least one particular usage context, to control placement of multiple workflow processes for the IoT deployment over a plurality of distributed locations within the IoT infrastructure, and to manage execution of the workflow processes at the distributed locations in accordance with one or more constraints of the particular usage context. The IoT deployment comprises one or more IoT platforms each configured to interact with a different set of IoT devices. The placement of multiple workflow processes over the plurality of distributed locations illustratively provides a designated distribution of data, services, applications and analytics for the IoT deployment in the particular usage context.

Distributed management optimization for IoT deployments

An apparatus in one embodiment comprises a processing platform implementing a data set discovery engine. The data set discovery engine comprises a data set indexer configured to generate similarity indexes for a plurality of data sets, and a relativistic retriever coupled to the data set indexer and configured to obtain a suitability template for a query and to execute the query against one or more of the similarity indexes based at least in part on the suitability template. A given one of the similarity indexes comprises at least first and second auxiliary information generated from respective ones of at least first and second different similarity measures of a plurality of different similarity measures. The first and second similarity measures comprise selected ones of the plurality of different similarity measures that are supported by the data set discovery engine with the supported similarity measures comprising both frequency-based and non-frequency-based similarity measures.

Data set discovery engine comprising relativistic retriever

An apparatus comprises a processing platform implementing a unified framework for representation and processing of trigger, context, action and result (TCAR) associations and constituent elements thereof in relation to customer communications in an enterprise. The processing platform comprises a TCAR mapper, a TCAR reasoner, a TCAR action commander, and a TCAR repository. The TCAR mapper is configured to capture events and transmogrify corresponding TCAR associations for storage in the TCAR repository. The TCAR reasoner is configured to perform one or more reasoning operations over selected ones of the TCAR associations stored in the TCAR repository. The TCAR action commander is configured to orchestrate one or more actions determined at least in part by the one or more reasoning operations performed by the TCAR reasoner. A given one of the TCAR associations includes as its constituent elements a trigger, context, an action and a result, and may contain additional related information.

Processing platform implementing unified framework for trigger, context, action and result associations in relation to customer communications

An apparatus in one embodiment comprises an analytics platform and a controller. The controller is associated with the analytics platform and configured to control automated deployment of analytics workspaces of the analytics platform. The analytics platform includes or otherwise has access to sets of at least data ingestion engines, data containers, analytics tools and virtual resources. The controller is configured to deploy a given one of the analytics workspaces of the analytics platform as an at least partially user-specified combination of particular selected ones of the data ingestion engines, data containers, analytics tools and virtual resources. The controller may additionally be configured to monitor usage of the combination of particular selected ones of the data ingestion engines, data containers, analytics tools and virtual resources of the given analytics workspace, and to alter the combination responsive to the monitoring.

Analytics platform and associated controller for automated deployment of analytics workspaces

A data classification system is associated with a hybrid cloud comprising at least one private cloud and at least one public cloud. The data classification system comprises a data set classification model classifying data sets, a cloud classification model classifying the private and public clouds of the hybrid cloud, and mapping policies each specifying a particular mapping between one or more classes of the data set classification model and one or more classes of the cloud classification model. The data classification system classifies a received data set using the data set classification model, and determines for the received data set at least one cloud of the hybrid cloud to which the received data set should be directed for further processing based at least in part on a result of the classification of the received data set, the cloud classification model and a selected one of the mapping policies.

Data classification system for hybrid clouds

Example embodiments of the present invention relate to a method, a system, and a computer program product for federated service requests of a logical data store comprising a plurality of heterogeneous data stores. The method includes providing a data abstraction services layer as a control plane for federated service requests of a logical data store, comprising a plurality of heterogeneous data stores, by an application operating on a processing device, receiving a service request at the data abstraction services layer from the application operating on the processing device directed to the logical data store, and returning results of the service request from the data abstraction services layer to the application operating on the processing device.

Coordinated configuration, management, and access across multiple data stores

Methods and apparatus are provided for database migration. Source databases are migrated to a target data environment by receiving a user selection of one or more of the source databases; receiving a user selection of a target data environment profile; receiving a user selection of one or more storage options for the target data environment; and providing the user with an assessment of whether one or more of the selected source databases can be one or more of migrated and consolidated to the selected target data environment. A user can specify a backup and recovery profile for the migrated database. Metadata affinity can optionally be analyzed for a plurality of the selected source databases to identify an affinity correlation across the selected source databases. Scripts and/or transform blueprints can be generated for the selected target data environment (optionally based on rule-based best practices).

Methods and apparatus for database migration

Growing community of inventors

Lexington, MA, United States of America

David Stephen Reiner