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.

An intelligent hotspot scattering method includes learning request quantity curves of a plurality of URLs based on an artificial intelligence learning model and performing request quantity prediction on the plurality of URLs, determining a first URL from the plurality of URLs, determining a second URL from the plurality of URLs, and performing a hotspot scattering operation on the URLs. A predicted request quantity of the first URL is greater than or equal to a first predetermined request quantity threshold corresponding to the first URL. A request quantity of the second URL is not predictable and an actual request quantity of the second URL is greater than or equal to a second predetermined request quantity threshold corresponding to the second URL.

An intelligent hotspot scattering method includes learning request quantity curves of a plurality of URLs based on an artificial intelligence learning model and performing request quantity prediction on the plurality of URLs, determining a first URL from the plurality of URLs, determining a second URL from the plurality of URLs, and performing a hotspot scattering operation on the URLs. A predicted request quantity of the first URL is greater than or equal to a first predetermined request quantity threshold corresponding to the first URL. A request quantity of the second URL is not predictable and an actual request quantity of the second URL is greater than or equal to a second predetermined request quantity threshold corresponding to the second URL.

A computer-implemented method for the random-based leader election in a distributed network of data processing devices, said distributed network including a plurality of identified asynchronous processes, wherein all said identified processes or a subset thereof are running processes participating in the leader election, including the following steps: a) a random information is generated by each running process and shared with the other running processes, so that each running process maintains a set of said random information, b) a distributed random information is calculated by each running process from the set of random information by applying a first shared transformation function, so that the same distributed random information is made available to each running process, c) a designator of a single one of said running processes is calculated from the distributed random information by means of a second shared transformation function, d) said designator is used to elect a leader amongst said running processes.

A computer-implemented method for the random-based leader election in a distributed network of data processing devices, said distributed network including a plurality of identified asynchronous processes, wherein all said identified processes or a subset thereof are running processes participating in the leader election, including the following steps: a) a random information is generated by each running process and shared with the other running processes, so that each running process maintains a set of said random information, b) a distributed random information is calculated by each running process from the set of random information by applying a first shared transformation function, so that the same distributed random information is made available to each running process, c) a designator of a single one of said running processes is calculated from the distributed random information by means of a second shared transformation function, d) said designator is used to elect a leader amongst said running processes.

The disclosed technology includes techniques for secure access to data associated with an organization and includes providing a user device access to a user interface that is configured execute function requests. Upon receipt of a function request, a computer can access a predetermined portion of the organization's data, generate an output by executing the requested function based on the predetermined portion of the organization's data, and transmit the output to the user device.

The disclosed technology includes techniques for secure access to data associated with an organization and includes providing a user device access to a user interface that is configured execute function requests. Upon receipt of a function request, a computer can access a predetermined portion of the organization's data, generate an output by executing the requested function based on the predetermined portion of the organization's data, and transmit the output to the user device.

The application discloses an intelligent hotspot scattering method, an apparatus, a storage medium, and a computer device. The method comprises: learning a request quantity curve of a URL based on an artificial intelligence learning model and predicting a request quantity of the URL; determining, as a first URL, a URL of which the predicted request quantity of the URL is greater than or equal to a first predetermined request quantity threshold corresponding to the URL, and performing a first hotspot scattering operation on the URL; and determining, as a second URL, a URL of which the request quantity of the URL cannot be predicted and the actual request quantity is greater than or equal to a second predetermined request quantity threshold corresponding to the URL, and performing a second hotspot scattering operation on the URL. In the disclosed technical solutions, non-burst hotspot URL requests can be automatically predicted so that a first scattering operation can be performed in advance, and a second scattering operation can be performed on unpredictable burst hotspot URL requests, which speeds up the processing of hotspot businesses.

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.

Methods and architecture for load-correcting requests for serverless functions to reduce latency of serverless computing are provided. An example technique exploits knowledge that a given server node does not have a serverless function ready to run or is overloaded. Without further processing overhead or communication, the server node shifts the request to a predetermined alternate node without assessing a current state of the alternate node, an efficient decision based on probability that a higher chance of fulfillment exists at the alternate node than at the current server, even with no knowledge of the alternate node. In an implementation, the server node refers the request but also warms up the requested serverless function, due to likelihood of repeated requests or in case the request is directed back. An example device has a front-end redirecting server and a backend serverless system in a single component.