Abstract: Disclosed dynamic schema mapping systems and methods monitor network traffic between different microservices and train mapping models based on the monitored network traffic using unsupervised training. This training of the mapping models generates a probability distribution tensor that shows the probabilistic associations of different key-value pairs of the schemas of different microservices. The trained mapping models are used to map a schema from a source microservice to another schema at a destination microservice. Should the translated schema be incompatible with the destination microservice, a semi-supervised approach is taken to make the translated schema compatible. The trained models may be reinforced (e.g., the probability distribution tensor may be updated) as more network traffic is collected and analyzed.

Abstract: A computer-implemented system and method for achieving strong consistency in an eventually consistent distributed system. The system comprises a database and an application server hosting a computer software product that a user subscribes to in order to access the product via a user device over a network. The application server receives a first service request from the user device for obtaining a first set of features associated with the user. The application server may ensure that the user receives a useable and correct set of features.