Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.