Abstract: A system for governing user privacy data may include detecting a dark data source in a data ecosystem including data sources storing user privacy data. The system may further include identifying user information from the data ecosystem according to a lineage of datasets and scanning the dark data source by executing a natural language processing engine to identify existence of and content of user privacy data in the dark data source based on the user information. The system may further include selecting a target user privacy data compliance policy from user privacy data compliance policies based on a geographical region associated with the dark data source and detecting non-compliance in protecting the user privacy data in the dark data source based on the target user privacy data compliance policy. The system may further include, in response to the non-compliance, processing the user privacy data to eliminate the non-compliance.

Abstract: A system for governing user privacy data may include detecting a dark data source in a data ecosystem including data sources storing user privacy data. The system may further include identifying user information from the data ecosystem according to a lineage of datasets and scanning the dark data source by executing a natural language processing engine to identify existence of and content of user privacy data in the dark data source based on the user information. The system may further include selecting a target user privacy data compliance policy from user privacy data compliance policies based on a geographical region associated with the dark data source and detecting non-compliance in protecting the user privacy data in the dark data source based on the target user privacy data compliance policy. The system may further include, in response to the non-compliance, processing the user privacy data to eliminate the non-compliance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for engineering a data analytics platform using machine learning are disclosed. In one aspect, a method includes the actions of receiving data indicating characteristics of data for analysis, analysis techniques to apply to the data, and requirements of users accessing the analyzed data. The actions further include accessing provider information that indicates computing capabilities of a respective data analysis provider, analysis techniques provided by the respective data analysis provider, and real-time data analysis loads of the respective data analysis provider. The actions further include applying the characteristics of the data, the analysis techniques, the requirements of the users, and the provider information, the analysis techniques, and the real-time data analysis loads to a model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for engineering a data analytics platform using machine learning are disclosed. In one aspect, a method includes the actions of receiving data indicating characteristics of data for analysis, analysis techniques to apply to the data, and requirements of users accessing the analyzed data. The actions further include accessing provider information that indicates computing capabilities of a respective data analysis provider, analysis techniques provided by the respective data analysis provider, and real-time data analysis loads of the respective data analysis provider. The actions further include applying the characteristics of the data, the analysis techniques, the requirements of the users, and the provider information, the analysis techniques, and the real-time data analysis loads to a model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for engineering a data analytics platform using machine learning are disclosed. In one aspect, a method includes the actions of receiving data indicating characteristics of data for analysis, analysis techniques to apply to the data, and requirements of users accessing the analyzed data. The actions further include accessing provider information that indicates computing capabilities of a respective data analysis provider, analysis techniques provided by the respective data analysis provider, and real-time data analysis loads of the respective data analysis provider. The actions further include applying the characteristics of the data, the analysis techniques, the requirements of the users, and the provider information, the analysis techniques, and the real-time data analysis loads to a model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for engineering a data analytics platform using machine learning are disclosed. In one aspect, a method includes the actions of receiving data indicating characteristics of data for analysis, analysis techniques to apply to the data, and requirements of users accessing the analyzed data. The actions further include accessing provider information that indicates computing capabilities of a respective data analysis provider, analysis techniques provided by the respective data analysis provider, and real-time data analysis loads of the respective data analysis provider. The actions further include applying the characteristics of the data, the analysis techniques, the requirements of the users, and the provider information, the analysis techniques, and the real-time data analysis loads to a model.

Abstract: According to an example, a digital image may be processed by an ensemble of convolutional neural networks (CNNs) to classify objects in the digital image. For each CNN, a candidate architecture and candidate parameters may be selected to build a plurality of CNNs. Once it is determined that a predetermined number of CNNs, each having different values for the selected candidate parameters, meet a validation threshold, an ensemble of CNNs may be generated from the predetermined number of CNNs. The predictions from the ensemble of CNNs may then be aggregated to accurately classify the objects in the digital image.

Abstract: According to an example, a digital image may be processed by an ensemble of convolutional neural networks (CNNs) to classify objects in the digital image. For each CNN, a candidate architecture and candidate parameters may be selected to build a plurality of CNNs. Once it is determined that a predetermined number of CNNs, each having different values for the selected candidate parameters, meet a validation threshold, an ensemble of CNNs may be generated from the predetermined number of CNNs. The predictions from the ensemble of CNNs may then be aggregated to accurately classify the objects in the digital image.

Abstract: A system and method for optimizing collections processing is provided.

Abstract: An automatically reconfigurable high performance FPGA system that includes a hybrid FPGA network and an automated scheduling, partitioning and mapping software tool adapted to configure the hybrid FPGA network in order to implement a functional task. The hybrid FPGA network includes a plurality of field programmable gate arrays, at least one processor, and at least one memory. The automated software tool adapted to carry out the steps of scheduling portions of a functional task in a time sequence, partitioning a plurality of elements of the hybrid FPGA network by allocating or assigning network resources to the scheduled portions of the functional task, mapping the partitioned elements into a physical hardware design for implementing the functional task on the plurality of elements of the hybrid FPGA network, and iteratively repeating the scheduling, partitioning and mapping steps to reach an optimal physical hardware design.

Abstract: An automatically reconfigurable high performance FPGA system that includes a hybrid FPGA network and an automated scheduling, partitioning and mapping software tool adapted to configure the hybrid FPGA network in order to implement a functional task. The hybrid FPGA network includes a plurality of field programmable gate arrays, at least one processor, and at least one memory. The automated software tool adapted to carry out the steps of scheduling portions of a functional task in a time sequence, partitioning a plurality of elements of the hybrid FPGA network by allocating or assigning network resources to the scheduled portions of the functional task, mapping the partitioned elements into a physical hardware design for implementing the functional task on the plurality of elements of the hybrid FPGA network, and iteratively repeating the scheduling, partitioning and mapping steps to reach an optimal physical hardware design.