Among other things, an aspect includes a data storage system associated with a provider entity and storing data on behalf of a client entity, the data being accessible from the data storage system by the client entity, a data interface enabling access by the provider entity to the data of the data storage system, and an analysis engine maintained by the provider entity to, at times determined by the analysis engine, access the data using the data interface, analyze the data, and generate results of the analysis for use by the client entity.

In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

Technical solutions are described for selecting components from multiple vendors for a system. An example computer-implemented method includes receiving, by a processor, an environment map that identifies a first component placeholder of the system. The computer-implemented method further includes identifying, by the processor, that the first component placeholder interfaces with a second component placeholder of the system. The computer-implemented method further includes determining, by the processor, a role assigned to the first component placeholder, where the role indicates a type of data that a first component at the first component placeholder communicates with a second component at the second component placeholder. The computer-implemented method further includes determining, by the processor, a list of components for selecting the first component, where each component from the list of components matches the role assigned to the first component placeholder.

A system and method for expediting fulfillments of online ordered dual-commerce available deliverables at one or more expedited service areas (ESAs) of a networked venue facility associated with an ecommerce service provider (ESP) where a sequenced deliverable pickup fulfillment or procurement access is provided within a specified time-window, and/or by delivery fulfillments made to specific locations.

A system and method for expediting fulfillments of online ordered dual-commerce available deliverables at one or more expedited service areas (ESAs) of a networked venue facility associated with an ecommerce service provider (ESP) where a sequenced deliverable pickup fulfillment or procurement access is provided within a specified time-window, and/or by delivery fulfillments made to specific locations.

A method includes storing a parameter related to a user, storing descriptive data for multiple identifiers, and indexing multiple events. Each event corresponds to a physical object supplied to a user on behalf of an entity. The method includes identifying a first set of identifiers based on commonality among the descriptive data. The method includes training a machine learning model for the first set of identifiers based on event data from within a predetermined epoch. The method includes receiving an indication of a selected identifier and determining a first intake metric of the selected identifiers using the machine learning model. The method includes determining a second intake metric of the selected identifier and the parameter and transforming the user interface according to the first and second intake metrics. The first intake metric represents an amount of resources expected to be received during a second epoch subsequent to the predetermined epoch.

The present invention provides a carbon value assessment (“carbon valuation”) system, which includes an information processing device that evaluates an amount of greenhouse gas (“GHG”) reduction expected to occur by a technology project in a currency value, on the basis of: estimated reduction of greenhouse gas (“estimated GHG reduction amount”) which is an amount of GHG reduction presumed to be induced by the technology project that is a target to be assessed; a calculation period of the GHG reduction during which the GHG reduction occurs due to the technology project; a discount rate that quantifies possible risks occurring in the technology project; and an emission permit price.

A system and method are disclosed for interactive product assortment planning and visualization by receiving product attribute values for items of a product assortment is disclosed. Embodiments include displaying icons on an interactive visualization, connecting the icons with transferable demand links, identifying items to be removed from a product assortment, and transporting items among one or more supply chain entities.

The disclosure discloses a method for analyzing educational big data on the basis of maps. The method includes acquiring educational resource data and storing the educational resource data into databases according to certain data structures; constructing theme map layers for each analysis theme, classifying and indexing data according to the analysis themes, and superimposing the theme map layers onto base maps to form data maps; analyzing data of the theme map layers according to the analysis themes and acquiring theme analysis results; extracting the data of the multiple theme map layers in target regions, fusing the data and acquiring region analysis results; acquiring learning preference of users; combining the learning preference of the users according to content of user requests and searching the region analysis results in response to the user requests. The disclosure further discloses a system for analyzing the educational big data on the basis of the maps.