Abstract: Examples of the present disclosure relate to an AI-supported CDN. In examples, a data processing engine processes log data of a CDN node according to a model to identify an issue. An issue indication is provided to a solution generation engine, which generates a set of solutions to automatically resolve the issue. The set of solutions is provided to a solution implementation engine, which iteratively implements solutions to resolve the issue using solution implementation information associated with a given solution. Thus, the data processing engine need not have knowledge regarding the specific hardware and/or software used within the CDN. Similarly, the solution generation engine need not have knowledge of the structure of the CDN and/or configuration of devices associated with the identified issue, such that the solution implementation engine provides a layer of abstraction between a solution and the implementation-specific details used to implement the solution within the CDN.

Abstract: Examples of the present disclosure relate to an AI-supported CDN. In examples, a data processing engine processes log data of a CDN node according to a model to identify an issue. An issue indication is provided to a solution generation engine, which generates a set of solutions to automatically resolve the issue. The set of solutions is provided to a solution implementation engine, which iteratively implements solutions to resolve the issue using solution implementation information associated with a given solution. Thus, the data processing engine need not have knowledge regarding the specific hardware and/or software used within the CDN. Similarly, the solution generation engine need not have knowledge of the structure of the CDN and/or configuration of devices associated with the identified issue, such that the solution implementation engine provides a layer of abstraction between a solution and the implementation-specific details used to implement the solution within the CDN.

Abstract: According to one aspect, embodiments of the invention provide a system for identifying elastic products in a retail environment, the system comprising a network interface configured to be coupled to a communication network, a product analysis module coupled to the network interface and configured to communicate with a server of each one of a plurality of retail stores in the retail environment via the network interface and the communication network, and a database coupled to the product analysis module, wherein the product analysis module is further configured to receive signals from each server of the plurality of retail stores including experience based information related to sales of a plurality of products in each one of the plurality of retail stores, and profile a group of the plurality of products as elastic products based on the experience based information related to the sales of the plurality of products.

Abstract: The present disclosure deals with a method, system and computer readable medium to calculate suggested data, distribute the suggested data over a distributed computing system and update terminals the new suggested data. The application gathers data from shared folders on the central server and calculates the suggested data. The application pushes all the calculated data into a centrally located database. A second application retrieves the calculated data in spreadsheet form. A data is updated according to preferences and stored the in the centrally located database. The second application transmits the suggested data to select computing systems. The computing system updates the terminals with the new suggested data.

Abstract: A system for promotional forecasting in a retail environment is provided. The system includes at least one processor coupled to a memory storing sales history information associated with a product, an interface configured to receive a promotion configuration profile including an indication of the product and a state of a plurality of promotion variables, and a promotional forecasting component. The promotional forecasting component is configured to determine a base sales quantity of the product based on the sales history information, determine a lift factor for each promotion variable, the lift factor indicative of an effect of the promotion variable on the base sales quantity, determine a total lift factor for the promotion configuration profile based on the lift factor for each promotion variable and the state of each promotion variable, and determine a forecasted sales quantity for the product based on the base sales quantity and the total lift factor.

Abstract: According to one aspect, embodiments of the invention provide a system for identifying inelastic products, the system comprising an interface, a markdown analysis module, and a database, wherein the markdown analysis module is further configured to receive signals from each server of a plurality of retail stores including product sales information, calculate, based on the received information, the total expected markdown for each retail store, identify, based on the total expected markdown of each retail store, an outlier store that has a total expected markdown greater than a threshold, identify a sister store that has at least one similar characteristic to the outlier store and less total expected markdown than the outlier store, compare expected markdowns of the outlier store and the sister store, and identify, based on the comparison between the expected markdowns of the outlier and sister stores, at least one inelastic product in the outlier store.