Abstract: Implementation of a guided rules editor comprises displaying a view of a user interface (UI) of the guided rules editor to a user on the client device. Metadata of an organization to which the user belongs is fetched and used to identify which data of the organization is eligible for rule creation and attributes of the metadata are used to determine a layout of the one or more UI selection elements. One or more UI selection elements are rendered in one or more positions in the view on a display screen according to the layout and at least a portion of the one or more UI selection elements are populated with the data of the organization. Responsive to receiving user input of the one or more UI selection elements, an organization-specific rule is generated and saved to a data repository for execution by a rules engine after rule compilation.

Abstract: Implementation of a guided rules editor comprises displaying a view of a user interface (UI) of the guided rules editor to a user on the client device. Metadata of an organization to which the user belongs is fetched and used to identify which data of the organization is eligible for rule creation and attributes of the metadata are used to determine a layout of the one or more UI selection elements. One or more UI selection elements are rendered in one or more positions in the view on a display screen according to the layout and at least a portion of the one or more UI selection elements are populated with the data of the organization. Responsive to receiving user input of the one or more UI selection elements, an organization-specific rule is generated and saved to a data repository for execution by a rules engine after rule compilation.

Abstract: Implementation of a guided rules editor includes a rule compilation process comprising receiving, by an event handler, a compile rules event from a process external to the rule compiler. A set of rules is retrieved from a database and loading the set of rules into a memory by the event handler. Responsive to identifying any template rules, data is bound to the template rules. Both the template rules and any non-template rules are translated into a rules language specification. A rule compilation process is performed asynchronously from a rule execution process to translate the rule language specification into a knowledge base that is to be used as input by a rule execution process. A compile completion event is emitted at compile completion.

Abstract: A rules engine (RE) may operate in conjunction with a database providing functionality, such as transactional support in data access environments on behalf of tenants. The database may have a data repository accessible by multiple tenants, and tenants may have a private context. RE rules may be defined in the data repository having an extension point corresponding to an extension defined in the private context. Execution of database functionality may affect data defined in the database. Execution of RE rules corresponding to database functionality affects corresponding data associated with the RE. Various techniques, e.g., fact handles, event listeners, etc. may be used to coordinate tracking and synchronizing changes between RE data and/or the database. A flag or other indicator may signify state preservation between multiple calls to the database and/or the RE, e.g., to support analysis involving transactions having overlapping intermediary results such as results from performing data lookups.

Abstract: A rules engine (RE) may operate in conjunction with a database providing functionality, such as transactional support in data access environments on behalf of tenants. The database may have a data repository accessible by multiple tenants, and tenants may have a private context. RE rules may be defined in the data repository having an extension point corresponding to an extension defined in the private context. Execution of database functionality may affect data defined in the database. Execution of RE rules corresponding to database functionality affects corresponding data associated with the RE. Various techniques, e.g., fact handles, event listeners, etc. may be used to coordinate tracking and synchronizing changes between RE data and/or the database. A flag or other indicator may signify state preservation between multiple calls to the database and/or the RE, e.g., to support analysis involving transactions having overlapping intermediary results such as results from performing data lookups.

Abstract: Implementation of a guided rules editor comprises displaying a view of a user interface (UI) of the guided rules editor to a user on the client device. Metadata of an organization to which the user belongs is fetched and used to identify which data of the organization is eligible for rule creation and attributes of the metadata are used to determine a layout of the one or more UI selection elements. One or more UI selection elements are rendered in one or more positions in the view on a display screen according to the layout and at least a portion of the one or more UI selection elements are populated with the data of the organization. Responsive to receiving user input of the one or more UI selection elements, an organization-specific rule is generated and saved to a data repository for execution by a rules engine after rule compilation.

Abstract: A rules engine (RE) may operate in conjunction with a database providing functionality, such as transactional support in data access environments on behalf of tenants. The database may have a data repository accessible by multiple tenants, and tenants may have a private context. RE rules may be defined in the data repository having an extension point corresponding to an extension defined in the private context. Execution of database functionality may affect data defined in the database. Execution of RE rules corresponding to database functionality affects corresponding data associated with the RE. Various techniques, e.g., fact handles, event listeners, etc. may be used to coordinate tracking and synchronizing changes between RE data and/or the database. A flag or other indicator may signify state preservation between multiple calls to the database and/or the RE, e.g., to support analysis involving transactions having overlapping intermediary results such as results from performing data lookups.

Abstract: Apparatus and method for a scalable pricing engine. A scalable pricing engine is operable in a low-scale mode and a high-scale or scalable mode to process pricing data in response to pricing requests. The pricing engine operates in a first runtime environment in the low-scale mode in which it can service pricing requests up to a threshold number and in which the pricing data can be updated and tested. The pricing engine also operates in a second runtime environment which is a scalable runtime supporting pricing requests above the specified threshold. The scalable runtime and pricing engine rely on a scalable read-only data service which distributes a read-only copy of at least a portion of the pricing data from the primary database, a high-scale cache to reduce access latency, data processing and network resource allocations, and data reduction techniques when processing requests and responses.

Abstract: Apparatus and method for asynchronous pricing. For example, some implementations include an asynchronous pricing service in addition to the pricing service. When performing a first pricing operation on a first set of pricing data, the pricing service performs operations on a pricing engine. In response to a second pricing request at the asynchronous pricing service with a second set of pricing data, the second set of pricing data is subdivided into a plurality of portions and a corresponding plurality of pricing jobs are specified, which are independently executed by the pricing engine to produce a corresponding plurality of partial pricing results. The asynchronous pricing service aggregates the partial pricing results to generate a second pricing result.

Abstract: Embodiments are directed to a pluggable architecture for performance of pricing operations. An embodiment of a storage medium includes instructions for installing multiple pricing methods at a pricing service including a pluggable architecture for pricing methods, each pricing method including a set of pricing functions; receiving a pricing request for a sales transaction including one or more sales items, the pricing request including a pricing context for each sales item; and performing a price calculation for the sales transaction, including resolving a pricing method for each sales item, applying the set of pricing functions for the resolved pricing method for each sales item, the application of the set of pricing functions being based at least in part on the pricing context for the sales item, calculating a price for each sales item based upon an outcome of the pricing functions, and aggregating the calculated prices for the sales items.

Abstract: Pluggable pricing modules with functional extension points. A plurality of pricing methods are installed with a pricing service. The pricing service has a pluggable architecture to accept pricing methods including a set of pricing functions, and one or more extension points that provide programmatic hooks within an operational flow of the pricing service to provide optional interfaces to functional modules external to the pricing service. In response to a pricing request for a sales transaction, the pricing request including a pricing resolving selected pricing method for each sales item of the one or more sales items from the plurality of pricing methods. At least one of the one or more extension points are used to call an external functional module to interact with the operational flow of the pricing service. Aggregated results are provided to the source of the pricing request.

Abstract: Preservation of price calculation data in performance of price calculation operations is described. An example of a system includes one or more processors, a memory, an API to receive requests, a pricing service to generate price calculations, and price data functions to perform operations on price calculation data, wherein in response to a request for price calculation for a first transaction the system is to generate a unique transaction identifier; record context data for the price calculation; calculate a pricing result for each of one or more sales items and collect price calculation data for each sales item; aggregate the pricing results to generate a final pricing result, generate a price calculation data set for the first transaction including at least the transaction identifier, the context data, and the price calculation data for the sales items, and store the generated price calculation data set in the memory.

Abstract: Embodiments regard pricing operation using artificial intelligence for price adjustment. An embodiment of one or more mediums include instructions for receiving a request at a pricing platform for pricing of sales items in a sales transaction, including a first sales item; generating a price for the first sales item; and determining whether a dynamic price adjustment function is enabled for the first sales item, and, if so, performing the dynamic adjustment price function for the first sales item, including accessing a trained neural network trained for price adjustments based at least in part on training data including news data from one or more sources and data regarding pricing, receiving a dynamic price adjustment for the first sales item from the trained neural network, and applying the dynamic price adjustment to the generated price to produce an adjusted price for the first sales item.

Abstract: Embodiments regard pricing operations utilizing a uniform abstract sales transaction model. An embodiment of one or more storage mediums include instructions for receiving a pricing request for pricing of one or more sales items, the pricing request including a sales transaction, the sales transaction being a data structure that includes an input parameter, the input parameter including input data; directing the sales transaction to a pricing engine for the sales operation; performing pricing for each sales item of one or more sales items based on the sales transaction to generate a pricing output; generating an output parameter utilizing the input parameter, and inserting the pricing output into the output parameter; and generating a pricing response including the sales transaction with the output parameter, the sales transaction data structure being unchanged by the pricing engine.

Abstract: Embodiments regard determination and optimization of pricing plans for sales transactions. An embodiment of one or more storage mediums include instructions for receiving a request for pricing of a sales transaction including one or more sales items; storing the sales transaction in memory for analysis; performing a static analysis of the sales transaction, including scanning the received sales transaction in memory to analyze structure and content of the sales transaction; generating an initial pricing plan for the sales transaction based on the static analysis; commencing a price calculation for the sales transaction; identifying one or more inefficiencies in the initial pricing plan during the price calculation; and dynamically modifying the initial pricing plan based at least in part on the identified one or more inefficiencies to improve performance of pricing for the sales transaction.

Abstract: Embodiments are directed to a pluggable architecture for performance of pricing operations. An embodiment of a storage medium includes instructions for installing multiple pricing methods at a pricing service including a pluggable architecture for pricing methods, each pricing method including a set of pricing functions; receiving a pricing request for a sales transaction including one or more sales items, the pricing request including a pricing context for each sales item; and performing a price calculation for the sales transaction, including resolving a pricing method for each sales item, applying the set of pricing functions for the resolved pricing method for each sales item, the application of the set of pricing functions being based at least in part on the pricing context for the sales item, calculating a price for each sales item based upon an outcome of the pricing functions, and aggregating the calculated prices for the sales items.

Abstract: Pluggable pricing modules with functional extension points. A plurality of pricing methods are installed with a pricing service. The pricing service has a pluggable architecture to accept pricing methods including a set of pricing functions, and one or more extension points that provide programmatic hooks within an operational flow of the pricing service to provide optional interfaces to functional modules external to the pricing service. In response to a pricing request for a sales transaction, the pricing request including a pricing resolving selected pricing method for each sales item of the one or more sales items from the plurality of pricing methods. At least one of the one or more extension points are used to call an external functional module to interact with the operational flow of the pricing service. Aggregated results are provided to the source of the pricing request.

Abstract: The present invention is related to a method and system for facilitating the integration of a plurality of dissimilar systems by allowing networks of integration framework installations and/or other compatible B2B servers to inter-operate across corporate enterprise boundaries to integrate the disparate systems operating within each corporate enterprise.

Abstract: A solution is provided for associating network traffic traversing on a networked environment according to a selected category item, such as a user name or other network entity identity-related information, by using a monitor device. The solution includes: obtaining user information from the directory service by obtaining at least one set of user object attributes from the directory service; identifying at least one authentication exchange packet from packets traversing on the networked environment; extracting a user ID and a network address from the authentication exchange packet; filtering or selecting packets traversing on the network environment that each have a network address equivalent to the extracted network address; and associating packets that were selected with user information having a name attribute equivalent to the extracted user ID.