Amadeus IT Group has been granted a patent for a method using machine learning to determine primary storage locations for data records in a distributed system. The method involves training a model with metadata values and topology information, then using the model to identify storage locations based on incoming requests. GlobalData’s report on Amadeus IT Group gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Amadeus IT Group, was a key innovation area identified from patents. Amadeus IT Group's grant share as of May 2024 was 39%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning model for determining primary storage location in distributed system
A recently granted patent (Publication Number: US12001448B2) discloses a computer-implemented method for maintaining a distributed system with multiple databases located in different geographic locations. The method involves training a machine learning model using training feature vectors derived from prior metadata values of requests associated with data records. The model is then used to determine primary storage databases for new requests by processing metadata values and executing the model. If the request is a write request, the data record is written to the identified primary storage database, while for read requests, the data record is attempted to be read from the identified database. In cases where the record is not found, alternative databases are explored. The system also includes a cartography server that updates the machine learning model based on discrepancies between identified and target primary storage databases, ensuring accuracy and efficiency in database selection.
Furthermore, the patent describes a distributed system comprising multiple databases, mapper servers, and a cartography server connected via a distributed data network. The cartography server generates a machine learning model by training it with training feature vectors derived from prior requests' metadata values. This model is then transmitted to mapper servers, which use it to determine primary storage databases for data records based on new requests. The system ensures efficient data storage and retrieval by leveraging machine learning to optimize database selection, especially in scenarios where the primary storage database may be remote from the request origin. The use of geographic elements in training and prediction feature vectors enhances the system's ability to handle data with geographic significance, such as travel information, by associating specific identifiers with locations and service providers.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
