Emc IP Holding Company LLC

Emc Corporation

David I Dietrich

Stephen J Todd

Rhonda Baldwin

Wayne A Pauley, Jr

David Stephen Reiner

Beibei Yang

Barry William Heller

Milind Arun Bhandarkar

Ronald Wilfred Reidy

Data set valuation techniques are provided. For example, a request is obtained from a client to utilize one or more cloud computing services managed by at least one service provider. A valuation is determined for delivering the one or more requested cloud computing services to the client. The valuation determination includes determining a valuation of one or more data sets associated with the one or more cloud computing services.

Data set valuation for service providers

Example embodiments of the present invention include a method, a system, and a computer program product for provisioning a computing system in accordance with an automated data analytics lifecycle. The method includes defining a holistic context of an analytics lifecycle for an analytic problem. One or more data sets for solving the analytic problem and at least a portion of original data in the one or more data sets may be conditioned to generate conditioned data. The method also includes selecting a model to analyze at least one of the original data and the conditioned data and executing the selected model on at least one of a portion of the original data and a portion of the conditioned data to confirm an adequacy of the selected model. Results of execution may be communicated to at least one entity for provisioning computing resources to implement the selected model.

Holistic methodology for big data analytics

A dynamic content controller is configured for communication with one or more data sources. The dynamic content controller comprises an analytics engine and a personalization engine coupled to the analytics engine. The analytic engine is configured to analyze multiple types of disaggregated data collected from the one or more data sources during a current content browsing session. The personalization engine is configured to adapt content to be presented based at least in part on the analysis of the disaggregated data. The adaptation of the content to be presented is performed during the current content browsing session.

Dynamic content control in an information processing system

Data generated in accordance with execution of one or more phases of an automated data analytics lifecycle associated with a given data science project is collected. At least a portion of the collected data is analyzed. At least one future outcome associated with the given data science project is predicted based at least in part on the collecting and analyzing steps.

Data science project automated outcome prediction

A work package is obtained defining a data analytic plan for analyzing a given data set associated with a given data problem. The work package is generated in accordance with an automated data analytics lifecycle. The data analytic plan and the given data set are analyzed. Based on at least a portion of results of the analysis, at least one of a cost parameter and a time parameter is computed for one or more computing resource configurations proposed for implementing the data analytic plan. One of the one or more computing resource configurations is selected based on at least one of the cost parameter and the time parameter. A computing resource infrastructure is caused to be provisioned in accordance with the selected computing resource configuration. The above steps are performed on one or more processing elements associated with a computing system.

Data analytics computing resource provisioning based on computed cost and time parameters for proposed computing resource configurations

A dynamic content controller is configured for communication with one or more data sources. The dynamic content controller comprises an analytics engine and a personalization engine coupled to the analytics engine. The analytics engine is configured to analyze cultural data collected from the one or more data sources during a current content browsing session. The personalization engine is configured to adapt content to be presented based at least in part on the analysis of the cultural data. The adaptation of the content to be presented is performed during the current content browsing session.

Dynamic content control in an information processing system based on cultural characteristics

A first work package defining a data analytic plan associated with a given data problem is obtained. The first work package is generated in accordance with an automated data analytics lifecycle and is implemented in a provisioned system. A second work package defining a revised data analytic plan is obtained. A set of differences between the first work package and the second work package is determined. Cost and time parameters, associated with modifying the provisioned system to implement the set of differences between the first work package and the second work package, are computed. The set of differences and the computed cost and time parameters are presented. The provisioned system is automatically re-configured in accordance with at least a portion of the set of differences and based on the computed cost and time parameters.

Computing resource re-provisioning during data analytic lifecycle

A hypothesis associated with one or more data analytics tasks is obtained. One or more data sets, relevant to the hypothesis, are extracted from one or more data sources, wherein relevance to the hypothesis is determined by one or more selected analytical methods. The one or more extracted data sets are transformed based on one or more characteristics of the hypothesis and one or more privacy constraints associated with one or more data analytics tasks. The above steps are performed to automate at least part of a data analytics lifecycle.

Hypothesis-centric data preparation in data analytics

An analytics controller is configured for communication with one or more data sources. The analytics controller comprises an analytic data focus representation module and a visualization generator coupled to the analytic data focus representation module, with the analytic data focus representation module being configured to derive a plurality of analytic data focus representations from the one or more data sources, and the visualization generator being configured to generate visualizations based at least in part on the analytic data focus representations. At least one of the analytic data focus representation module and the visualization generator may be further configured to establish a plurality of linkages with each such linkage associating one or more of the representations with one or more of the visualizations. The analytics controller may be part of a data management system implemented using one or more processing devices of a processing platform.

Analytic data focus representations for visualization generation in an information processing system

Information processing techniques for generating privacy ratings for services that are available via a cloud infrastructure, e.g., cloud services. For example, a method comprises the following steps. Data indicative of privacy attributable to at least one of a service and a provider of the service accessible in a cloud infrastructure is collected. A privacy rating is generated for at least one of the service and the provider of the service based on at least a portion of the collected data.

Privacy scoring for cloud services

A data analytic plan is defined for analyzing a given data set associated with a given data problem. A test data set and a training data set are obtained from the given data set associated with the given data problem. At least one model is executed to confirm an adequacy of the at least one model for the data analytic plan by fitting the at least one model on the training data set and evaluating the at least one model fitted on the training data set against the test data set. The defining, obtaining and executing steps are performed on one or more processing elements associated with a computing system and automate at least part of a data analytics lifecycle.

Data analytics lifecycle processes

An initial work package is obtained. The initial work package defines at least one hypothesis associated with a given data problem, and is generated in accordance with one or more phases of an automated data analytics lifecycle. A plurality of datasets is identified. One or more datasets in the plurality of datasets that are relevant to the at least one hypothesis are discovered. The at least one hypothesis is tested using at least a portion of the one or more discovered datasets.

Dataset discovery in data analytics

An initial data analytic plan for analyzing a given data set associated with a given data problem is defined. At least a portion of original data in the given data set is conditioned to generate conditioned data. At least one model is selected to analyze at least one of the original data and the conditioned data. The at least one selected model is executed on at least one of a portion of the original data and a portion of the conditioned data. Results of the model execution are communicated to at least one entity, the results comprising a refined data analytic plan for analyzing the given data set. One or more computing resources are provisioned to implement the refined data analytic plan. The defining, conditioning, selecting, executing, communicating and provisioning steps are performed on one or more processing elements associated with a computing system and automate a data analytics lifecycle.

Data analytics lifecycle automation

Techniques for aggregating hypotheses for use in data analytics. In one example, a method comprises the following steps. A plurality of hypotheses associated with one or more data analytics tasks are stored in a storage queue. Two or more hypotheses of the plurality of hypotheses are selected for aggregation. The selected two or more hypotheses are aggregated into a hypotheses group such that the selected two or more hypotheses of the hypothesis group are processed together using one or more common resources to perform at least one of the one or more data analytics tasks.

Hypotheses aggregation in data analytics

Classification of images or other types of high-resolution data is performed by a cluster-based data classification system. The system comprises a learner module, a classification director, and a complex classifier comprising a plurality of multi-outcome data classifiers. The classification director determines particular process rules and settings to be applied to a classification request, and the complex classifier is instantiated to process the classification request in accordance with the process rules and settings determined by the classification director. The process rules and settings are adapted under control of the learner module at least in part based on results obtained in processing the classification request and one or more additional classification requests. The cluster-based data classification system may be implemented on a processing platform comprising at least one Hadoop cluster, such that the classification is performed in a parallel manner across multiple processing devices utilizing MapReduce processing.

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Hopedale, MA, United States of America

David I Dietrich