Abstract: A device, system and method for providing provider objects from a cache is provided. Provider objects are received representing items provided by provider systems. The provider objects are categorized by reusability and/or context characteristics and stored as categorized. A request is received for one or more items provided by the one or more provider systems. A stored and categorized provider object is determined to meet criteria associated with the request, including one or more of a respective reusability and respective context characteristics of the provider object. In response to determining that the provider object, that meets the criteria, is estimated to be currently valid, a response corresponding to the provider object is provided.

Abstract: A device, system and method for training machine learning models using messages associated with provider objects is provided. One or more computing devices: receives messages associated with provider objects representing items provided by provider systems, the messages having more than one format; stores harmonized objects corresponding to the messages, the harmonized objects generated using mappings of harmonized data of the harmonized objects to message data of the messages, the harmonized objects having common formats for a harmonized object type; extracts, from the harmonized objects, for a given machine learning model, given machine learning training data; generates, for the given machine learning model, using the given machine learning training data, at least one machine learning classifier; and provides the at least one machine learning classifier to the given machine learning model at one or more servers configured to implement the given machine learning model on received provider objects.

Abstract: A device, system and method for providing provider objects from a cache is provided. Provider objects are received representing items provided by provider systems. The provider objects are categorized by reusability and/or context characteristics and stored as categorized. A request is received for one or more items provided by the one or more provider systems. A stored and categorized provider object is determined to meet criteria associated with the request, including one or more of a respective reusability and respective context characteristics of the provider object. In response to determining that the provider object, that meets the criteria, is estimated to be currently valid, a response corresponding to the provider object is provided.

Abstract: A device, system and method for training machine learning models using messages associated with provider objects is provided. One or more computing devices: receives messages associated with provider objects representing items provided by provider systems, the messages having more than one format; stores harmonized objects corresponding to the messages, the harmonized objects generated using mappings of harmonized data of the harmonized objects to message data of the messages, the harmonized objects having common formats for a harmonized object type; extracts, from the harmonized objects, for a given machine learning model, given machine learning training data; generates, for the given machine learning model, using the given machine learning training data, at least one machine learning classifier; and provides the at least one machine learning classifier to the given machine learning model at one or more servers configured to implement the given machine learning model on received provider objects.

Abstract: A method, apparatus, and program product suitable for use in the travel industry utilize a dynamic and hierarchical generic data mapping model to facilitate the publication of traveler profile data from a source system, such as a profile management system, to a plurality of downstream target systems. A generic data mapping model may be utilized to dynamically build a target-specific publication data mapping, e.g., in association with the publication of a traveler profile, such that the traveler profile may be transformed into a target format using the dynamically built publication data mapping.