Method and system for emergent data processing

Abstract

A method and system for emergent data processing are described. A system having one or more servers operable to handle retail data can receive content including customer data and product data. The content can be normalized and stored into a hyper-graph structure in the servers. The system can be used to select a portion of the hyper-graph structure based on a particular customer and to generate a recommendation for the particular customer based on the content in that portion of the hyper-graph structure. The system can also generate personal catalogs based on the information in the hyper-graph structure. The system can perform competitive analysis between products from different sources and include the results in the recommendations. Moreover, the system can perform a vertical analysis of consumable products to provide recommendations for tools or products that can be used in connection with the consumable products.

What is claimed is: 
     
         1 - 23 . (canceled) 
     
     
         24 . A method, comprising:
 receiving, via a server, data comprising data in one or more undocumented fields, wherein the received data comprises customer data and product data;   establishing interconnections between the received data in a hyper-graph structure, wherein the data in the one or more undocumented fields are placed unaltered in the hyper-graph structure, and wherein the data from the one or more undocumented fields are understood via their interconnections in the hyper-graph structure, and wherein the server comprises memory configured to maintain the hyper-graph structure;   selecting a portion of the hyper-graph structure based on similarity of components of data for a particular customer to components of customer data present in the hypergraph structure; and   generating a recommendation for the particular customer based on the content in the selected portion of the hyper-graph structure.   
     
     
         25 . The method of  claim 24 , wherein the recommendation is one of a product, an article, an image, a catalog, a recipe, a question, an answer, and a video. 
     
     
         26 . The method of  claim 24 , wherein the method comprises filtering the recommendation based on one or both of a merchant black-listing by theme and sentiment information about the particular customer. 
     
     
         27 . The method of  claim 24 , wherein the method comprises generating the recommendation for the particular customer based on business parameters stored in the server, wherein the business parameters include one or more of margin, revenue, competitive positioning, costumer acquisition, customer retention, and customer activity. 
     
     
         28 . The method of  claim 24 , wherein:
 the particular customer is one of a plurality of customers,   the plurality of customers are characterized by multiple personas,   each persona is based on characteristics and data from the plurality of customers,   the system determines which of the multiple personas is prevailing at a particular time or for a particular interaction for the particular customer, and   the prevailing persona is based on characteristics and data from the plurality of customers including the particular customer.   
     
     
         29 . The method of  claim 24 , wherein the method comprises:
 normalizing the received data through abstraction and semantic generalization; and   after normalization, storing the received data into the hyper-graph structure.   
     
     
         30 . The method of  claim 24 , wherein the particular customer is defined in an emergent data processing system using dynamic data dimensions. 
     
     
         31 . The method of  claim 24 , wherein the method comprises:
 generating an electronic message that includes the recommendation; and   tracking an interaction of the particular customer with the recommendation in the electronic message.   
     
     
         32 . A method, comprising:
 receiving content comprising a plurality of components of customer data and product data, wherein one or more of the components comprise data in one or more undocumented fields;   normalizing, in a memory of a server, the plurality of components of the received content;   storing the normalized plurality of components of the received content to establish interconnections in a hyper-graph structure, wherein the hyper-graph structure comprises a plurality of components of content for related customer data and product data from a plurality of sources, and wherein the data in the one or more undocumented fields are placed unaltered in the hyper-graph structure, and wherein the data from the one or more undocumented fields are distinguished via their interconnections in the hyper-graph structure;   selecting a portion of the hyper-graph structure based on similarity of components of data for a particular customer to components of customer data present in the hypergraph structure; and   constructing a personal catalog for the particular customer based on the content in the selected portion of the hyper-graph structure.   
     
     
         33 . The method of  claim 32 , wherein the personal catalog includes historical interaction information of the particular customer. 
     
     
         34 . The method of  claim 32 , wherein the personal catalog includes a recommendation that includes one or more of a product, an article, an image, a catalog, a recipe, a question, an answer, and a video. 
     
     
         35 . The method of  claim 34 , wherein the method comprises:
 determining one or more items related to a recommendation; and   providing the one or more items in the personal catalog.   
     
     
         36 . The method of  claim 32 , wherein the method comprises linking the personal catalog to one or more additional catalogs corresponding to the particular customer. 
     
     
         37 . The method of  claim 32 , wherein the selected portion of the hyper-graph structure corresponds to a cohort of customers that includes the particular customer. 
     
     
         38 . A method, comprising:
 receiving content comprising a plurality of components of customer data and product data, wherein one or more of the components comprise data in one or more undocumented fields;   storing, in a memory of a server, the plurality of components of the received content to establish interconnections in a hyper-graph structure of the received data, wherein the data in the one or more undocumented fields are placed unaltered in the hyper-graph structure, and wherein the data from the one or more undocumented fields are identified via their interconnections in the hyper-graph structure;   filling in particular components missing from data of a first customer in the hyper-graph structure using known values of the particular components in data of a second customer in the hyper-graph structure;   selecting a portion of the hyper-graph structure based on similarity of components of data for a particular customer to components of customer data present in the hypergraph structure;   comparing data stored in the at least one server corresponding to one commercial entity with data stored in the at least one server corresponding to another commercial entity, the data being compared comprising product data; and   constructing a personal catalog from at least a recommendation for the particular customer based on the content in the selected portion of the hyper-graph structure and the comparison, for transmission to a device of the particular customer for viewing.   
     
     
         39 . The method of  claim 38 , wherein the comparison is based on one or both of product pricing and product quality. 
     
     
         40 . The method of  claim 38 , wherein the data being compared including one or more of product data, a review, a blog, a video, a picture, an activity, a question, and an answer. 
     
     
         41 . The method of  claim 38 , wherein the method comprises:
 generating an electronic message that includes the recommendation; and   tracking an interaction of the particular customer with the recommendation in the electronic message.   
     
     
         42 . The method of  claim 38 , wherein the selected portion of the hyper-graph structure corresponds to a cohort of customers that includes the particular customer. 
     
     
         43 . The method of  claim 38 , wherein the method comprises normalizing the customer data and the product data through abstraction and semantic generalization, prior to storing the customer data and the product data into the hyper-graph structure.