A data processing method and apparatus are disclosed. The data processing method includes: receiving, by a first edge computing node in an edge computing cluster, a container image update instruction; obtaining, by the first edge computing node, a to-be-updated container image; and sending, by the first edge computing node, the to-be-updated container image to another edge computing node in the edge computing cluster. The first edge computing node is an edge computing node in the edge computing cluster, the edge computing cluster includes at least two edge computing nodes, and the container image update instruction is used to instruct the at least two edge computing nodes in the edge computing cluster to update a container image. In this way, the time required for downloading the container image is reduced.

A computer-implemented method routes service requests to services in a service framework provided by a plurality of hosts. The method comprises receiving a service request for a service in the service framework and discovering a plurality of candidate hosts that host the service. The plurality of candidate hosts are a subset of the plurality of hosts. The method further comprises selecting a candidate host from the plurality of candidate hosts based on measured latencies for the plurality of candidate hosts and routing the service request to the selected candidate host.

A system and method in accordance with example embodiments may include systems and methods for a cloud-based analytics platform. The cloud-based analytics platform may allow the manual and automatic uploading to and/or downloading from a cloud server. The platform may include single sign-on (SSO) capabilities such that a user may have one set of credentials to access data from the cloud-based analytics and/or data stored locally. The platform may include data validation and processing in order to provide real-time feedback on uploads based on file type, file size, access rights, extracted data, and transformed data.

A system and method in accordance with example embodiments may include systems and methods for a cloud-based analytics platform. The cloud-based analytics platform may allow the manual and automatic uploading to and/or downloading from a cloud server. The platform may include single sign-on (SSO) capabilities such that a user may have one set of credentials to access data from the cloud-based analytics and/or data stored locally. The platform may include data validation and processing in order to provide real-time feedback on uploads based on file type, file size, access rights, extracted data, and transformed data.

The present disclosure provides an edge computing network, a data transmission method and apparatus, a device and a storage medium. The edge computing network includes: a first edge node configured to receive streaming media data sent by a first client, store the streaming media data onto a first storage resource, and store information of the first storage resource onto a second storage resource, both the first storage resource and the second storage resource being located in the edge computing network; and a second edge node configured to receive a request sent by a second client, acquire the information of the first storage resource from the second storage resource, acquire the streaming media data from the first storage resource based on the information of the first storage resource, and send the streaming media data to the second client.

In general, embodiments of the present invention provide for ranking data objects using machine learning and scoring to configure a graphical interface. In this regard, a machine learning model is applied to data object attributes to generate data object relevance scores for the candidate data objects. Additionally, a respective personalization score for the candidate data objects is generated based at least in part on a compounded ratio and a geometric mean of the compounded ratio. The data object relevance scores for the candidate data objects are modified based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects. The candidate data objects are then ranked based at least in part on the modified object relevance scores to generate a ranked data object set and to facilitate configuration of a graphical interface.

In general, embodiments of the present invention provide for ranking data objects using machine learning and scoring to configure a graphical interface. In this regard, a machine learning model is applied to data object attributes to generate data object relevance scores for the candidate data objects. Additionally, a respective personalization score for the candidate data objects is generated based at least in part on a compounded ratio and a geometric mean of the compounded ratio. The data object relevance scores for the candidate data objects are modified based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects. The candidate data objects are then ranked based at least in part on the modified object relevance scores to generate a ranked data object set and to facilitate configuration of a graphical interface.

Embodiments include a multi-tenant cloud system that receives a request for an authenticate action for a user. Embodiments create an authenticate target action and register a cache listener for a cache that includes a filter to listen for a target action response that is responsive to the authenticate target action, the filter listing a plurality of bridges assigned to an on-premise active directory. Embodiments randomly select one of the plurality of bridges and sends the authenticate target action to the active directory via the selected bridge. Embodiments wait for a cache callback and, at the cache callback, receive a target action response that includes a result of the authenticate action.

The present disclosure relates to technologies for a sensor network, machine-to-machine (M2M) communications, or machine type communications (MTC), and Internet of things (IoT), and may be utilized in intelligent services, based on the above technologies, such as a smart home, a smart building, smart cities, smart cars or connected cars, smart grids, healthcare, smart electronics, advanced medical services, public safety network communications through fusion and convergence with conventional IT technologies and various industries. Data transmission control between a group of uncoordinated electronic devices in a computer network including the use of transmission authorization request messages and transmission authorization grant messages.

In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.