Implementing static loaders and savers for the transfer of local and distributed data containers to and from storage systems can be difficult because there are so many different configurations of output formats, data containers and storage systems. Described herein is an extensible componentized data transfer framework for performant and scalable authoring of data loaders and data savers. Abstracted local and distributed workflows drive selection of plug-ins that can be composed by the framework into particular local or distributed scenario loaders and savers. Reusability and code sparsity are maximized.

In some embodiments, a service platform that facilitates artificial intelligence model and data collection and collection may be provided. Input/output information derived from machine learning models may be obtained via the service platform. The input/output information may indicate (i) first items provided as input to at least one model of the machine learning models, (ii) first prediction outputs derived from the at least one model's processing of the first items, (iii) second items provided as input to at least another model of the machine learning models, (iv) second prediction outputs derived from the at least one other model's processing of the second items, and (v) other inputs and outputs. The input/output information may be provided via the service platform to update a first machine learning model. The first machine learning model may be updated based on the input/output information being provided as input to the first machine learning model.

In some embodiments, a service platform that facilitates artificial intelligence model and data collection and collection may be provided. Input/output information derived from machine learning models may be obtained via the service platform. The input/output information may indicate (i) first items provided as input to at least one model of the machine learning models, (ii) first prediction outputs derived from the at least one model's processing of the first items, (iii) second items provided as input to at least another model of the machine learning models, (iv) second prediction outputs derived from the at least one other model's processing of the second items, and (v) other inputs and outputs. The input/output information may be provided via the service platform to update a first machine learning model. The first machine learning model may be updated based on the input/output information being provided as input to the first machine learning model.

A device identification device (100) includes: a device feature value extractor (1-1, 1-2) to routinely extract one or more device feature values of an unknown device; a model identifier (6) to identify a model of the unknown device; a change pattern generator (2) to generate one or more change patterns of the extracted one or more device feature values; and a device similarity calculator (3) to compare each of the generated one or more change patterns with each of change patterns of known devices, to calculate a device similarity therebetween, and identify the unknown device, when the maximum value of the calculated device similarities is equal to or greater than a first threshold, as the known device indicating the maximum value.

Provided is a manner of determining a performance indicator score for an entity. A method includes identifying a set of result indicators that contribute to a target result for a defined process. The set of result indicators include a first result indicator, a second result indicator, and a third result indicator. The method also includes determining a first score for the first result indicator, a second score for the second result indicator, and a third score for the third result indicator. Further, the method includes applying a first weight to the first score, a second weight to the second score, and a third weight to the third score. A master performance indicator score is determined based on a combination of the first score, the second score, and the third score as a function of the first weight, the second weight, and the third weight.

Provided is a manner of determining a performance indicator score for an entity. A method includes identifying a set of result indicators that contribute to a target result for a defined process. The set of result indicators include a first result indicator, a second result indicator, and a third result indicator. The method also includes determining a first score for the first result indicator, a second score for the second result indicator, and a third score for the third result indicator. Further, the method includes applying a first weight to the first score, a second weight to the second score, and a third weight to the third score. A master performance indicator score is determined based on a combination of the first score, the second score, and the third score as a function of the first weight, the second weight, and the third weight.

A system and method receive a plurality of crypto profiles that include customizable rules for different cryptocurrencies and operating state information that initialize containerized lending applications. The system and method ink the crypto profiles to a matching engine before the containerized lending applications and the matching engine match a plurality of borrower requests for a debt or an equity denominated in a cryptocurrency to lending requests. The system and method collect cryptocurrency payments in response to the use of the debt or the equity by the borrower. Each containerized lending application include executable software, runtime code, system tools, and system libraries that enable the containerized applications to run on two or more computing environments without modification.

A system and method receive a plurality of crypto profiles that include customizable rules for different cryptocurrencies and operating state information that initialize containerized lending applications. The system and method ink the crypto profiles to a matching engine before the containerized lending applications and the matching engine match a plurality of borrower requests for a debt or an equity denominated in a cryptocurrency to lending requests. The system and method collect cryptocurrency payments in response to the use of the debt or the equity by the borrower. Each containerized lending application include executable software, runtime code, system tools, and system libraries that enable the containerized applications to run on two or more computing environments without modification.

A cluster compute server stores different types of data at different storage volumes in order to reduce data duplication at the storage volumes. The storage volumes are categorized into two classes: common storage volumes and dedicated storage volumes, wherein the common storage volumes store data to be accessed and used by multiple compute nodes (or multiple virtual servers) of the cluster compute server. The dedicated storage volumes, in contrast, store data to be accessed only by a corresponding compute node (or virtual server).

A cluster compute server stores different types of data at different storage volumes in order to reduce data duplication at the storage volumes. The storage volumes are categorized into two classes: common storage volumes and dedicated storage volumes, wherein the common storage volumes store data to be accessed and used by multiple compute nodes (or multiple virtual servers) of the cluster compute server. The dedicated storage volumes, in contrast, store data to be accessed only by a corresponding compute node (or virtual server).