The present teaching relates to a method, system and programming for operating a data storage. The data storage comprises of different portions including: a first portion having a plurality of metadata objects stored therein, each of the metadata objects being associated with a filter and corresponding to a range of keys, wherein at least one of the metadata objects is associated with a data structure, and a second portion having a plurality of files stored therein, each of the plurality of files being associated with one of the plurality of metadata objects; The data storage synchronizes a scan request with respect to one or more write requests based on a parameter associated with the scan request and each of the one or more write requests.

A remote distributed system for processing resource center operations by extracting one or more interaction entity profiles from one or more resource entity systems, store the one or more interaction entity profiles in a repository in the memory of the distributed remote networks systems, identifying that at least one third party entity system is requesting permission to access at least one interaction entity profile, where in the at least one third party entity system is associated with a resource center, authenticating the at least one third party system and displaying, via a graphical user interface, the at least one interaction entity profile on the at least one third party system, wherein the at least one interaction entity profile is used by the at least one third party entity to process interactions associated with the resource entity and an interaction entity associated with the at least one interaction entity profile.

A remote distributed system for processing resource center operations by extracting one or more interaction entity profiles from one or more resource entity systems, store the one or more interaction entity profiles in a repository in the memory of the distributed remote networks systems, identifying that at least one third party entity system is requesting permission to access at least one interaction entity profile, where in the at least one third party entity system is associated with a resource center, authenticating the at least one third party system and displaying, via a graphical user interface, the at least one interaction entity profile on the at least one third party system, wherein the at least one interaction entity profile is used by the at least one third party entity to process interactions associated with the resource entity and an interaction entity associated with the at least one interaction entity profile.

A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives from a client device an image and a user identifier associated with a user of the client device. Based on the user identifier, the program further retrieves a set of historical transactions associated with the user, the program also uses a first machine learning model to predict a string based on the image. The program further uses a second machine learning model to predict a set of digits based on the image. Based on the set of historical transaction data, the string, and the set of digits, the program also determines a data value for a field in a form.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives from a client device an image and a user identifier associated with a user of the client device. Based on the user identifier, the program further retrieves a set of historical transactions associated with the user, the program also uses a first machine learning model to predict a string based on the image. The program further uses a second machine learning model to predict a set of digits based on the image. Based on the set of historical transaction data, the string, and the set of digits, the program also determines a data value for a field in a form.

Embodiments of the present disclosure may include a method for credit underwriting, the method including receiving a dataset of user details. Embodiments may also include creating a convolutional neural network (CNN) with the dataset of user details. In some embodiments, the convolutional neural network organizes at least a portion of the dataset of user details into a layered and weighted dataset. Embodiments may also include creating an enriched layered and weighted dataset. Embodiments may also include executing a back-propagation operation to remove at least a portion of the layered and weighted data from the layered and weighted dataset. Embodiments may also include receiving a plurality of enriched layered and weighted datasets.

The present disclosure discloses a task processing method and a distributed computing framework. A specific embodiment of the method includes: parsing an expression corresponding to a distributed computing task, and constructing task description information corresponding to the distributed computing task, the task description information being used to describe a corresponding relationship between an operator and a distributed dataset, and the operator acting on at least one of the distributed dataset or distributed datasets obtained by grouping the distributed dataset; determining, based on the task description information, a distributed dataset the operator acting on; and performing distributed computing on the distributed dataset the operator acting on using the operator. In the distributed computing, the acting scope and nesting relationship of the operator is described by constructing a topology.

The present disclosure discloses a task processing method and a distributed computing framework. A specific embodiment of the method includes: parsing an expression corresponding to a distributed computing task, and constructing task description information corresponding to the distributed computing task, the task description information being used to describe a corresponding relationship between an operator and a distributed dataset, and the operator acting on at least one of the distributed dataset or distributed datasets obtained by grouping the distributed dataset; determining, based on the task description information, a distributed dataset the operator acting on; and performing distributed computing on the distributed dataset the operator acting on using the operator. In the distributed computing, the acting scope and nesting relationship of the operator is described by constructing a topology.