Computerized systems and methods for graph data modeling

Abstract

Systems, methods, and computer-readable media are provided for graph data modeling. In accordance with one implementation, a method is provided that includes operations performed by at least one processor. The operations of the method include receiving raw data and determining a model for the raw data, wherein the model defines the graph structure for the raw data. The method also includes converting the raw data to fit the model, and generating at least a portion of a graph based on the raw data and the model, wherein the graph produces modeled data. The method also includes archiving the graph.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method for converting data into graph structures, the method including:
 receiving, by a server processor, data from a user device, wherein the data corresponds with at least one or more data types;   determining whether the data includes a repeating pattern;   segmenting the data based on the determined repeating pattern;   determining a model for each of the segmented data; and   sending a first model of a first segment of data to a receiving entity for generating a graph structure, the graph structure comprising the segmented data recallable by executing a data request.   
     
     
         22 . The method of  claim 21 , wherein determining the model comprises:
 identifying a plurality of stored models including required fields and field restrictions;   determining the required fields of each of the plurality of stored models; and   determining whether the individualized segments of data includes the required fields and satisfies the field restrictions.   
     
     
         23 . The method of  claim 21 , further including:
 identifying portions of the segmented data for each part of the graph structure.   
     
     
         24 . The method of  claim 21 , further including:
 generating, based on required and optional modeled data and at least one of a node, an edge, and a property.   
     
     
         25 . The method of  claim 21 , further including:
 receiving additional data;   determining, based on the additional data, an update to the graph structure;   performing, based on the update, at least one action on the graph structure to create an updated graph structure, wherein the at least one action is at least one of: editing a property of a node of the graph structure, deleting a property, an edge, or a node of a graph structure, or adding a new node, edge or property to the graph structure; and   archiving the updated graph structure.   
     
     
         26 . The method of  claim 21 , further including:
 verifying the segmented data by determining required fields for the segmented data;   identifying an input model; and   determining a storage format.   
     
     
         27 . The method of  claim 21 , further including:
 receiving a request for modeled data;   identifying the graph entities corresponding to the modeled data; and   converting the identified graph entities to modeled data.   
     
     
         28 . A system for converting data into a graph structure, comprising:
 a storage device that stores instructions; and   at least one processor that executes instructions for:   receiving, by a server processor, data from a user device, wherein the data corresponds with at least one or more data types;   determining whether the data includes a repeating pattern;   segmenting the data based on the determined repeating pattern;   determining a model for each of the segmented data; and   sending a first model of a first segment of data to a receiving entity for generating a graph structure, the graph structure comprising the segmented data recallable by executing a data request.   
     
     
         29 . The system of  claim 28 , wherein the at least one processor is further configured for:
 identifying a plurality of stored models including required fields and field restrictions;   determining the required fields of each of the plurality of stored models; and   determining whether the individualized segments of data includes the required fields and satisfies the field restrictions.   
     
     
         30 . The system of  claim 28 , wherein the at least one processor is further configured for:
 identifying portions of the segmented data for each part of the graph structure.   
     
     
         31 . The system of  claim 28 , wherein the at least one processor is further configured for:
 generating, based on required and optional modeled data and at least one of a node, an edge, and a property.   
     
     
         32 . The system of  claim 28 , wherein the at least one processor is further configured for:
 receiving additional data;   determining, based on the additional data, an update to the graph structure;   performing, based on the update, at least one action on the graph structure to create an updated graph structure, wherein the at least one action is at least one of: editing a property of a node of the graph structure, deleting a property, an edge, or a node of a graph structure, or adding a new node, edge or property to the graph structure; and   archiving the updated graph structure.   
     
     
         33 . The system of  claim 28 , wherein the at least one processor is further configured for:
 verifying the segmented data by determining required fields for the segmented data;   identifying an input model; and   determining a storage format.   
     
     
         34 . The system of  claim 28 , wherein the at least one processor is further configured for:
 receiving a request for modeled data;   identifying the graph entities corresponding to the modeled data; and   converting the identified graph entities to modeled data.   
     
     
         35 . A non-transitory computer-readable medium storing instructions, the instructions configured to cause at least one processor to perform operations comprising:
 receiving, by a server processor, data from a user device, wherein the data corresponds with at least one or more data types;   determining whether the data includes a repeating pattern;   segmenting the data based on the determined repeating pattern;   determining a model for each of the segmented data; and   sending a first model of a first segment of data to a receiving entity for generating a graph structure, the graph structure comprising the segmented data recallable by executing a data request.   
     
     
         36 . The non-transitory computer-readable medium of  claim 35 , wherein determining the model further comprises:
 identifying a plurality of stored models including required fields and field restrictions;   determining the required fields of each of the plurality of stored models; and   determining whether the individualized segments of data includes the required fields and satisfies the field restrictions.   
     
     
         37 . The non-transitory computer-readable medium of  claim 35 , further including:
 identifying portions of the segmented data for each part of the graph structure.   
     
     
         38 . The non-transitory computer-readable medium of  claim 35 , further including:
 generating, based on required and optional modeled data and at least one of a node, an edge, and a property.   
     
     
         39 . The non-transitory computer-readable medium of  claim 35 , wherein the at least one processor is further configured for:
 receiving additional data;   determining, based on the additional data, an update to the graph structure;   performing, based on the update, at least one action on the graph structure to create an updated graph structure, wherein the at least one action is at least one of: editing a property of a node of the graph structure, deleting a property, an edge, or a node of a graph structure, or adding a new node, edge or property to the graph structure; and   archiving the updated graph structure.   
     
     
         40 . The non-transitory computer-readable medium of  claim 35 , wherein the at least one processor is further configured for:
 verifying the segmented data by determining required fields for the segmented data;   identifying an input model; and   determining a storage format.