Abstract: A portable, self-contained beverage apparatus includes a container assembly having a known storage capacity for storing a consumable liquid, and a dispensing assembly disposed within the container assembly that dispenses variable, non-zero quantities of additives into the consumable liquid. The dispensing assembly includes multiple apertures structured and arranged to retain vessels containing the additives to be dispensed into the consumable liquid. The beverage apparatus also includes a level sensor disposed within the container assembly that determines a consumable liquid level of the consumable liquid stored in the container assembly. In certain embodiments, one or more positive displacement pumping mechanisms are configured to pump additive liquid from additive containers into a beverage chamber.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: A method comprises storing first transaction data for a first transaction producing a first version of a first dataset, second transaction data for a second transaction transforming a first version of the first dataset to a second version of the first dataset, and third transaction data for a third transaction transforming the second version of the first dataset to a third version of the first dataset; storing dependency information indicating a first dependency of the third transaction on the second transaction and a second dependency of the second transaction on the first transaction; receiving a first instruction to revoke a first permission of a first user to access the second version of the first dataset; automatically revoking a certain permission of the first user to access the third version of the first dataset based on the dependency information.

Abstract: A method comprises creating metadata identifying columns of tables and column operations of one or more data transforms of the columns in a data pipeline and including links to code segments in human-readable form corresponding to the one or more data transforms; executing a build job that effects the one or more data transforms on one or more datasets to generate one or more derived datasets; causing, after the executing, a presentation of a graphical user interface (GUI) including a graphical representation of the one or more data transforms based on the metadata, wherein the method is performed by one or more processors.

Abstract: Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Abstract: A computer-implemented system and method for data revision control in a large-scale data analytic systems. In one embodiment, for example, a computer-implemented method comprises the operations of storing a first version of a dataset that is derived by executing a first version of driver program associated with the dataset; and storing a first build catalog entry comprising an identifier of the first version of the dataset and comprising an identifier of the first version of the driver program.

Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein.

Abstract: Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein.

Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein.

Abstract: A computer-implemented system and method for data revision control in a large-scale data analytic systems. In one embodiment, for example, a computer-implemented method comprises the operations of storing a first version of a dataset that is derived by executing a first version of driver program associated with the dataset; and storing a first build catalog entry comprising an identifier of the first version of the dataset and comprising an identifier of the first version of the driver program.

Abstract: A computer-implemented system and method for data revision control in a large-scale data analytic systems. In one embodiment, for example, a computer-implemented method comprises the operations of storing a first version of a dataset that is derived by executing a first version of driver program associated with the dataset; and storing a first build catalog entry comprising an identifier of the first version of the dataset and comprising an identifier of the first version of the driver program.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Abstract: Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: A method of determining the volume of organic material in a composting device includes rotating a mixing paddle in a composting bin that stores the organic material; monitoring the torque from a motor that rotates the mixing paddle as the mixing paddle rotates; and determining the height of the organic material in the composting bin based on the motor torque and the angular displacement of the mixing paddle. The resulting height can be used to determine the volume of the organic material.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein.