Patent Publication Number: US-2018033027-A1

Title: Interactive user-interface based analytics engine for creating a comprehensive profile of a user

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to the field of data analytics, and in particular relates to a system and method to provide for an interactive user-interface based analytics engine for identifying, segmenting, correlating, combining and profiling complex user data across multiple internal and external data sources, and creating a single profile of a user. 
     BACKGROUND OF THE INVENTION 
     Decision making functions of an enterprise require user knowhow for relevant recommendations and responsiveness. With the proliferation of diverse sources, for instance, web, mobile, enterprise transaction data, Point of Sale (PoS) sources, kiosk, wearables etc. there is an abundance of user interaction data available across disparate channels. Information related to user interactions on diverse channels, therefore, would significantly change the way in which enterprises perform decision making. Enterprise systems, therefore, not only require access to internal sources but also access to various external sources, including real-time operational information of users for a comprehensive and holistic understanding of user requirements. 
     Creation and management of electronic association of user data across diverse channels is one of the challenges faced by existing enterprise system architecture. Data processing systems and database systems in an enterprise system architecture need to be equipped to channelize and compute the plethora of user data across diverse channels efficiently and in real-time. 
     In light of the above, there is a need for a system and method that provides for an enhanced graphical user-interface based analytics engine for processing user data across enterprise and external interaction channels for creating and providing access to a single view of a user. There is a need for a system and method for efficiently identifying real-time, accurate, comprehensive and actionable details of users from diverse interaction channels. Also, there is a need for a system and method for efficient processing and handling of multi-structured data from diverse interaction channels and building a comprehensive profile of the user. 
    
    
     
       BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS 
       The present invention is described by way of embodiments illustrated in the accompanying drawings wherein: 
         FIG. 1  illustrates a block diagram of an interactive user-interface based analytics engine for creating a comprehensive profile of a user, in accordance with an embodiment of the present invention; 
         FIG. 2  is a detailed block diagram of the interactive user-interface based analytics engine, in accordance with an embodiment of the present invention; 
         FIG. 2 a    is a screenshot of an exemplary graphical user-interface display illustrating a representative list of users and user data; 
         FIG. 2 b    is a screenshot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s transaction data based on analysis of data stored in the enterprise databases; 
         FIG. 2 c    is a screenshot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s transaction and profile data based on analysis of data stored in the enterprise databases; 
         FIGS. 2 d  and 2 e    illustrate a screen shot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s data from external systems databases; 
         FIG. 2 f    is an exemplary representation of variables ascertained based on analysis of data and linking of variables across field columns, in accordance with various embodiments of the present invention; 
         FIG. 2 g    is a screenshot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s comprehensive profile, in accordance with various embodiments of the present invention; 
         FIG. 3  is a flowchart illustrating a method of operating an interactive user-interface based analytics engine, in accordance with various embodiments of the present invention; and 
         FIG. 4  illustrates an exemplary computer system in which various embodiments of the present invention may be implemented. 
       SUMMARY OF THE INVENTION 
       A system for generating a hypergraph representative of a comprehensive profile of the user, by invocation of an interactive user-interface by an end-user via a client device is provided. The system comprises a memory storing program instructions and a processor executing program instructions stored in the memory. The system further comprises a user segmentation engine in communication with the processor and configured to generate and render a first graphical representation of a selected user&#39;s transactional behavior, based on an analysis of one or more parameters associated with the selected user stored in an enterprise database. The user segmentation engine is further configured to retrieve data associated with the selected user from external systems for a predetermined time period. The system further comprises a data integration engine in communication with the processor and configured to determine a first set of variables from the retrieved data and classify the first set of variables into one or more data fields. The classification results in the variables across relevant data fields being linkable in accordance with one or more predetermined types of data categories associated with the selected user. Further, the data integration engine is configured to generate new data fields for realizing classification of additional retrieved data. The number of new data fields are generated based on an analysis of the additional retrieved data. The generation of new data field columns is triggered after the classification of the first set of variables. The data integration engine is further configured to determine a second set of variables from the retrieved data and classify the second set of variables into the new one or more data fields. The classification results in the variables across the relevant new data fields being linkable in accordance with the one or more predetermined types of data categories associated with the selected user. Furthermore, the data integration engine is configured to generate two or more graphical representations by linking the variables across the relevant new data fields, respectively, in accordance with the one or more predetermined types of data categories associated with the selected user. Finally, the data integration engine is configured to analyze the two or more generated graphical representations, and generate a hypergraph by integrating the two or more graphical representations and the first graphical representation. Based on the hypergraph a comprehensive profile of the selected user is generated. 
       A computer-implemented method of operating an interactive user-interface based analytics engine for generating a hypergraph representative of a comprehensive profile of the user, by invocation of said interactive user-interface by an end-user, is provided. The method comprises generating and rendering a first graphical representation of a selected user&#39;s transactional behavior, based on an analysis of one or more parameters associated with the selected user stored in an enterprise database. Furthermore, the method comprises retrieving data associated with the selected user from external systems for a predetermined time period. The method further comprises determining a first set of variables from the retrieved data. Further, the method comprises classifying the first set of variables into one or more data fields, the classification resulting in the variables across relevant data fields being linkable in accordance with one or more predetermined types of data categories associated with the selected user. The method further comprises generating new data fields for realizing classification of additional retrieved data, the number of new data fields being generated based on an analysis of the additional retrieved data, wherein the generation of new data field columns is triggered after the classification of the first set of variables. Furthermore, the method comprises determining a second set of variables from the retrieved data. Further, the method comprises classifying the second set of variables into the new one or more data fields, the classification resulting in the variables across the relevant new data fields being linkable in accordance with the one or more predetermined types of data associated with the selected user. The method further comprises generating two or more graphical representations by linking the variables across the relevant new data fields, respectively, in accordance with the one or more predetermined types of data categories associated with the selected user. Finally, the method comprises analyzing the two or more generated graphical representations and generating a hypergraph by integrating the two or more graphical representations and the first graphical representation. Based on the hypergraph a comprehensive profile of the selected user is generated. 
       A computer program product comprising a non-transitory computer-readable medium having computer-readable program code stored thereon, the computer-readable program code comprising instructions that, when executed by a processor, cause the processor to generate and render a first graphical representation of a selected user&#39;s transactional behavior, based on an analysis of one or more parameters associated with the selected user stored in an enterprise database. Further, the processor retrieves data associated with the selected user from external systems for a predetermined time period. Furthermore, the processor determines a first set of variables from the retrieved data. The processor classifies the first set of variables into one or more data fields. The classification results in the variables across relevant data fields being linkable in accordance with one or more predetermined types of data categories associated with the selected user. The processor further generates new data fields for realizing classification of additional retrieved data, the number of new data fields being generated based on an analysis of the additional retrieved data, wherein the generation of new data fields is triggered after the classification of the first set of variables. Furthermore, the processor determines a second set of variables from the retrieved data and classifies the second set of variables into the new one or more data fields, the classification resulting in the variables across the relevant new data fields being linkable in accordance with the one or more predetermined types of data associated with the selected user. Further, the processor generates two or more graphical representations by linking the variables across the relevant new data fields, respectively, in accordance with the one or more predetermined types of data categories associated with the selected user. Finally, the processor analyzes the two or more generated graphical representations, and generating a hypergraph by integrating the two or more graphical representations and the first graphical representation. Based on the hypergraph a comprehensive profile of the selected user is generated. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A system and method is provided for an interactive user-interface based analytics engine that provides for creation of a complete profile of a user by correlating and combining user details spread across multiple internal and external data sources. Advantageously, the system provides for deriving variables from data obtained from multiple internal and external systems and linking the variables in accordance with various embodiments of the present invention for generating a hypergraph representing real-time user information. 
     The disclosure is provided in order to enable a person having ordinary skill in the art to practice the invention. Exemplary embodiments herein are provided only for illustrative purposes and various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. The terminology and phraseology used herein is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed herein. For purposes of clarity, details relating to technical material that is known in the technical fields related to the invention have been briefly described or omitted so as not to unnecessarily obscure the present invention. 
     The present invention would now be discussed in context of embodiments as illustrated in the accompanying drawings. 
       FIG. 1  illustrates a block diagram of an interactive user-interface based analytics engine (system  102 ) for creating a comprehensive profile of a user, in accordance with various embodiments of the present invention. In an embodiment of the present invention, the system  102  may be implemented in a client-server architecture. The system  102  comprises a client device  104  and a user profile data analytics engine  106 . The client device  104  is a front-end component of the system  102  that includes a graphical user-interface  104   a  accessible by an end-user. The end-user may include a person interested in viewing and/or interacting with the system  102 . The user profile data analytics engine  106  is the back-end component of the system  102  which processes complex data obtained from multiple communication channels to generate a comprehensive view of the user&#39;s profile. Data includes, but is not limited to, structured data, unstructured data, temporal data, and multi-media datasets obtained from multiple-locations of the user, and from enterprise systems as well as external systems (third party systems). In various exemplary embodiments of the present invention, the data is analyzed, correlated and integrated, using advanced analytics solutions, and represented as a hypergraph structure. A hypergraph structure is a graphical structure where each edge of the graph can connect to any number of vertices that represent various parameters obtained from the processed user data. The comprehensive profile of the user is built using the hypergraph structure, where the hypergraph structure is an evolving structure that represents dynamic changes and real-time data of the user. This will be discussed in detail in later sections of the specification. 
     The client device  104  and the user profile data analytics engine  106  communicate over a communications network  108 . Examples of client device  104  include, but is not limited to, a personal computer, a laptop and any other wired or wireless terminal. The client device  104  is configured to operate a client application which is programmed to carry out the functionalities of the graphical user-interface  104   a  in accordance with various embodiments of the present invention. The user profile data analytics engine  106  is hosted on a server configured to operate a server-side application which is programmed to carry out the functionalities in accordance with various embodiments of the present invention. The communications network  108  may comprise interconnected software and hardware through which information may be transmitted and received. The communications network  108  may include a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), or any other type of wired or wireless network. In another embodiment of the present invention, the system  102  may be implemented in a cloud-computing environment. The functionalities of the graphical user-interface  104   a  and the user profile data analytics engine  106  may be presented to the end-user as Software as a Service (SaaS). 
       FIG. 2  is a detailed block diagram of the interactive user-interface based analytics engine (system  202 ) in accordance with various embodiments of the present invention. The user profile data analytics engine  220  of the system  202  comprises a data acquisition engine  204 , a user segmentation engine  206 , a data integration engine  208 , a user profile generation engine  210 , one or more enterprise databases  212 , and external system databases  214 . The end-user accesses the above mentioned engines ( 204 - 214 ) for creating a comprehensive profile of the user via a graphical user-interface  216  of the client device  222 . The client device  222  and the user profile data analytics engine  220  communicate over a communications network  218 . The data acquisition engine  204 , user segmentation engine  206 , data integration engine  208 , user profile generation engine  210 , enterprise databases  212 , and external system databases  214  and the graphical user-interface  216  execute various functionalities via a processor  224  using program instructions stored in a memory  226 . In various embodiments of the present invention, instructions to run the client application and the server-side application for operating the engines ( 204 - 210 ), the databases ( 212 ,  214 ) and the graphical user-interface  216  are stored in the memory  226  and executed by the processor  224 . 
     In an embodiment of the present invention, the data acquisition engine  204  connects the user profile data analytics engine  220  to various external systems via multiple communications channels. Examples of external systems include social media network servers, web servers, real estate database systems, credit history database systems, kiosk interaction systems, service contact center records, government database systems, user sensor based wearables, multitude of devices, appliances, and applications as well as multimedia datasets. The data acquisition engine  204  is configured to retrieve data from the various external systems and process the data for storing in the external system database  214 . In an exemplary embodiment of the present invention, data is collected from external systems through Application Programming Interface (API) based calls or from data uploaded from external systems. The collected data goes through a data cleaning phase, where the variables necessary for analytics is stored in a graph database (not shown). The graph database enables faster and easier mapping of variables retrieved from the external sources. The external system database  214  fetches the processed data from the graph database (not shown) for storing and further processing. 
     In an embodiment of the present invention, the user profile data analytics engine  220  is connected to the enterprise databases  212 . The enterprise database  212  may include an enterprise transaction database  212   a  and an enterprise User Relationship Management (CRM) database  212   b . The enterprise transaction database  212   a  includes, but is not limited to, user (user) transaction data, call center interaction data, Internet of Things (IoT) Analytics data etc. The enterprise User Relationship Management (CRM) database  212   b  includes, but is not limited to, user profile data, demographics data etc. 
     In operation, in an embodiment of the present invention, the end-user accesses the system  202  via the graphical user-interface  216  using access credentials. Subsequently, the end-user&#39;s access credentials are verified and the end-user is authenticated to access the system  102 . After the authentication procedure, the user segmentation engine  206  is invoked, which provides for a list of first order and second order employees of the enterprise who deal with each of the users (customers). The user segmentation engine  206  additionally accesses the enterprise transaction database  212   a  and enterprise CRM database  212   b  to analyze various parameters corresponding to the users displayed in the list. Examples of parameters may include, but is not limited to, risk analysis of user&#39;s engagements with the enterprise, quality and loan payment data, revenue generation information, product vs. user lifetime distribution data, credit score distribution vs. current balance data, data related to probability of attrition etc. Based on the analysis, the user segmentation engine  206  creates graphical representations using the data to illustratively depict trends of the users&#39; transactional behavior with respect to each of the parameters. In an exemplary embodiment of the present invention, the data is processed and analysed using a combination of proprietary as well as standard statistical analytics techniques. Post the above analysis, the graphical representations are generated.  FIG. 2 a    is a screenshot of an exemplary graphical user-interface display illustrating a representative list of users and user data. 
     The graphical user-interface  216  prompts the end-user to select a specific user (customer) whose comprehensive profile is desired to be generated by the end-user. Selection of a specific user triggers the user segmentation engine  206  to display trends of the selected user&#39;s transactional behavior with respect to each of the parameters (as discussed previously).  FIG. 2 b    is a screenshot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s transaction data based on analysis of data stored in the enterprise databases  212 . Further, the user segmentation engine  206  fetches data from the enterprise CRM database  212   b  and analyses the data for graphically representing details such as user age group, gender etc.  FIG. 2 c    is a screenshot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s transaction and profile data based on analysis of data stored in the enterprise databases  212 . 
     The user segmentation engine  206  is further configured to access and analyze data from the external systems databases  214 . In an exemplary embodiment of the present invention, the user segmentation engine  206  is configured to access data corresponding to the selected user from the external systems databases  214  for a predetermined time period. Examples of external systems database  214  include, but are not limited to, one or more databases that store user life event data from Facebook®, user conversations data from Twitter®, user profile data from Linkedin®, user location data from Instagram®, user credit history and loans data, user browsing history, user location data, legal data of the user, median salary records of the user, bank transaction details, life events from social media networks, property or any other asset information, mortgage information, discretionary spend estimate, census data, call centre data, and data obtained from user wearables. In an exemplary embodiment of the present invention, the user segmentation engine  206  performs a search, mapping and correlation of user data based on a series of matches including, but not limited to, user fields such as first name, last name, location, home address, telephone no., email address, photograph, common friends in multiple networks etc. The user segmentation engine  206  analyses the retrieved data using various machine learning techniques to parse various structured, unstructured data and translate into meaningful data.  FIGS. 2 d  and 2 e    illustrate a screen shot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s data from external systems databases  214 . The user segmentation engine  206 , thereafter, invokes the data integration engine  208 . 
     In an embodiment of the present invention, the data integration engine  208  retrieves the analyzed data from the user segmentation engine  206  for integrating the data in a meaningful manner. In particular, the data integration engine  208  classifies the analyzed data into various specific data fields and links the data to develop a hypergraph structure. In particular, the data integration engine  208  identifies a first set of variables from the retrieved data and organizes the variables into corresponding data fields. The variables in the corresponding data field are organized in a manner such that the variables can be linked across the data fields in a meaningful manner. The data fields are categorized in accordance with one or more predetermined types of data categories. Further, the data integration engine  208  creates new data fields for accommodating additional data retrieved from the external systems databases  214 . The data integration engine  208  derives a second set of variables from the additional retrieved data and classifies such variables into the new data fields. In various embodiments of the present invention, the hypergraph structure offers optimized, efficient, accurate, easy to use and high performance read and write of use data in a dynamic format. Consequently, the hypergraph structure accounts for frequent changes as well as capture of temporal user evolutionary data. This results in faster, more accurate analytics and insights. 
     By linking the variables across the data fields, the data integration engine  208  generates a hypergraph. In an exemplary embodiment of the present invention, a first set of data fields may include, but is not limited to, user id, social handle, transactional data, store id, contact centre interactions, sensor data from wearables, kiosk interactions, mobile location data, website metrics obtained from user&#39;s browsing history. A transaction or interaction graph is generated by linking variables in these field columns. A second set of data fields may include, but is not limited to, social handle, followers and followings, interactions, personality, likes, dislikes, comments, posts, tweets, status, pictures, videos, interests (music, movies, books)etc. A social graph is generated by linking variables in these field columns. A third set of data fields may include, but is not limited to, user id, social handle, first name, middle name, surname, age, gender, ethnicity, marital status, income, language, location, honours, awards, occupation, education etc. A profile graph is generated by linking variables in these field columns. A fourth set of data fields may include user id, social handle, changes in measures of demographic, psychographic and network activity variables over time. A temporal graph is generated by linking variables in these data fields. The data integration engine  208  analyzes the transaction or integration graph, social graph, profile graph, and the temporal graph to generate a hypergraph by further linking variables across the graphs.  FIG. 2 f    is an exemplary representation of variables ascertained based on analysis of data and linking of variables across the data fields, in accordance with various embodiments of the present invention. In an embodiment of the present invention, the data integration engine  208  uses advanced analytics solutions to analyze the generated hypergraph in order to derive correlation between user profile, various user activities and behavior in the context of enterprise systems and external or third party systems. Based on the analysis, for example, user lead score, risk sore, virality and velocity analysis score, social meme extraction score, theme and feature extraction score, social net promoter score, network analysis based ambassadors and detractors scores, global risk identification scores, advanced up-sale cross-sale analytics score, identity disambiguation score, influencer analysis score, life event detection and user psychographic analysis score may be generated. For example, velocity is a measure of how quickly the information is spreading. For a blog, it may be the number of new readers per hour, for a Twitter Source, it may be the number of re-tweets per hour, for a fan page on Facebook, it may be the number of fans added per day. Virality is a measure of the conversation spread and is measured by the number of people the message is spreading to. Volume is a measure of the total number of unique users reached. Sentiment analysis is a measure of positive or negative things being said. Sentiment is measured using advanced Natural Language Processing. Additionally, sentiments may also be identified along multiple dimensions, including, but not limited to, positive vs negative, strong vs weak, and active vs passive. Ambassador score is a measure of the number of positive influencers and detractor score is a measure of the number of negative influencers. Social net promoter score is derived from the number of ambassadors versus the number of detractors. The data integration engine  208  inputs the generated scores at appropriate data fields. 
     In an embodiment of the present invention, the user profile generation engine  210  communicates with the data integration engine  208  to build a comprehensive profile of the selected user. The user profile generation engine  210  prompts the end-user to select specific time intervals and builds a comprehensive profile of the selected user for the selected time intervals. In particular, the user profile generation engine  210  accesses the data integration engine  208  to analyze the generated hypergraph for the selected user. The comprehensive profile is generated by deriving information related to the selected user from the hypergraph, including but not limited to, his personality type (e.g. openness, extraversion, neuroticism, consciousness, and agreeableness), sentiment score, areas of interests, loan and credit dealings, social interactions, geographical data etc. Additionally, the user profile generation engine  210  also generates user clusters for each selected time interval by analyzing the hypergraph to ascertain user&#39;s relationships including, but not limited to, number of friends, followers, network centrality, groups, etc. across several social destinations. In various embodiments of the present invention, the user profile generation engine  210  displays the generated comprehensive profile via the graphical user-interface  216 .  FIG. 2 g    is a screenshot of an exemplary graphical user-interface display illustrating a representation of the selected user&#39;s comprehensive profile. The end-users may view the comprehensive profile for recommending products and services to the selected users. In various embodiments of the present invention, the user profile generation engine  210  generates reports, for instance a summary report and a page-wise detailed report. The reports may be generated in a PDF or HTML format for ease of reference and further processing. 
       FIG. 3  is a flowchart illustrating a method of operating an interactive user-interface based analytics engine (system), in accordance with various embodiments of the present invention. 
     At step  302 , data related to a selected user is accessed from enterprise databases, analyzed and presented in a graphical format. In an embodiment of the present invention, the end-user accesses the system via a graphical user-interface. Post authentication, the system provides a list of first order and second order employees of the enterprise who deal with each of the users. Various parameters corresponding to the users displayed in the list are analyzed from enterprise databases. Examples of parameters may include, but is not limited to, risk analysis of user&#39;s engagements with the enterprise, quality and loan payment data, revenue generation information, product vs. user lifetime distribution data, credit score distribution vs. current balance data, data related to probability of attrition etc. Based on the analysis, the system creates graphical representations using the data to illustratively depict trends of the user&#39;s transactional behavior with respect to each of the parameters. The graphical user-interface prompts the end-user to select a specific user whose comprehensive profile is desired to be generated by the end-user. Selection of a specific user triggers display of trends of the selected user&#39;s transactional behavior with respect to each of the parameters. Further, the system fetches and analyses data related to the selected user&#39;s profile information for graphically representing details such as user age group, gender etc. 
     At step  304 , data related to the selected user is accessed from external systems for a predetermined time period for analysis. In an exemplary embodiment of the present invention, external systems databases include, but are not limited to, one or more databases that store user life event data from Facebook®, user conversations data from Twitter®, user profile data from Linkedin®, user location data from Instagram®, user credit history and loans data, user browsing history, user location data, legal data of the user, median salary records of the user, bank transaction details, life events from social media networks, property or any other asset information, mortgage information, discretionary spend estimate, census data, and data obtained from user wearables. The retrieved data is analyzed using various machine learning techniques to parse various structured, unstructured data and translate into meaningful data. 
     At step  306 , the data from the enterprise databases and external systems databases is integrated to generate a hypergraph structure. In an embodiment of the present invention, the analyzed data is classified into various specific data fields based on one or more predetermined types of data categories. The classified data in the various data fields are linked to develop a hypergraph structure. In particular, a first set of variables from the retrieved data is determined and organized into corresponding data fields. The first set of variables in the corresponding data fields are classified in a manner such that the variables can be linked across the fields in a meaningful manner. The first set of data fields are categorized in accordance with one or more predetermined data categories. In an exemplary embodiment of the present invention, a first set of data fields may be categorized in accordance with a first type of predetermined data category, but is not limited to, user id, social handle, transactional data, store id, contact centre interactions, sensor data from wearables, kiosk interactions, mobile location data, website metrics obtained from user&#39;s browsing history. A transaction or interaction graph is generated by linking variables in these data fields. A second set of data fields may be categorized in accordance with a second type of predetermined data category, including, but is not limited to, social handle, followers and followings, interactions, personality, likes, dislikes, comments, posts, tweets, status, pictures, videos, interests (music, movies, books)etc. A social graph is generated by linking variables in these data fields. A third set of data fields may be categorized in accordance with a third type of predetermined data category, including, but is not limited to, user id, social handle, first name, middle name, surname, age, gender, ethnicity, marital status, income, language, location, honours, awards, occupation, education etc. A profile graph is generated by linking variables in these data fields. A fourth set of data fields may be categorized in accordance with a fourth type of predetermined data category, including, but is not limited to, user id, social handle, changes in measures of demographic, psychographic and network activity variables over time. A temporal graph is generated by linking variables in these data fields. The transaction or integration graph, social graph, profile graph, and the temporal graph are analyzed to generate a hypergraph by further linking variables across the graphs. In an embodiment of the present invention, a new set of data fields is generated by analyzing additional data that is retrieved from the external systems. After the classification of the first set of variables, the external system is monitored for additional data at predetermined time intervals. Based on the analysis, the new data fields are the generated. A second set of variables is generated from the additional retrieved data. Consequently, the second set of variables is classified into the new data fields. The classification takes place in a manner such that the second set of variables is linkable across the new data fields in a meaningful manner in accordance with the one or more predetermined data categories as described above in relation to the first set of data fields. 
     At step  308 , the generated hypergraph structure is analyzed to assign various correlation scores. In an embodiment of the present invention, the generated hypergraph is analyzed in order to derive correlation between user profile, various user activities and behavior in the context of enterprise systems and external or third party systems. Based on the analysis, for example, user lead score, risk score, virality and velocity analysis score, social meme extraction score, theme and feature extraction score, social net promoter score, network analysis based ambassadors and detractors scores, organizational reputation score, global risk identification scores, advanced up-sale cross-sale analytics score, identity disambiguation score, influencer analysis score, life event detection and user psychographic analysis score may be generated. The generated scores are then inputted at appropriate field columns and is automatically reflected on the hypergraph structure. 
     At step  310 , a comprehensive profile of the selected user is generated based on the hypergraph structure. In an embodiment of the present invention, specific time intervals are selected by the end-user via the graphical user-interface and a comprehensive profile of the selected user is generated for the selected time intervals. In particular, the comprehensive profile is generated by deriving information related to the selected user from the hypergraph, including but not limited to, his personality type (e.g. openness, extraversion, neuroticism, consciousness, and agreeableness), sentiment score, areas of interests, loan and credit dealings, social interactions, geographical data etc. Additionally, user clusters are generated for each selected time interval by analyzing the hypergraph to ascertain user&#39;s relationships including, but not limited to, number of friends, followers, network centrality, groups, etc. across several social destinations. In various embodiments of the present invention, the generated comprehensive profile is displayed via the graphical user-interface. The end-users may view the comprehensive profile for recommending products and services to the selected users. In various embodiments of the present invention, reports may be generated based on the comprehensive profile of the selected user. For instance, a summary report and a page-wise detailed report may be generated in a PDF or HTML format for ease of reference and further processing. 
       FIG. 4  illustrates an exemplary computer system in which various embodiments of the present invention may be implemented. 
     The computer system  402  comprises a processor  404  and a memory  406 . The processor  404  executes program instructions and is a real processor. The computer system  402  is not intended to suggest any limitation as to scope of use or functionality of described embodiments. For example, the computer system  402  may include, but not limited to, a programmed microprocessor, a micro-controller, a peripheral integrated circuit element, and other devices or arrangements of devices that are capable of implementing the steps that constitute the method of the present invention. In an embodiment of the present invention, the memory  406  may store software for implementing various embodiments of the present invention. The computer system  402  may have additional components. For example, the computer system  402  includes one or more communication channels  408 , one or more input devices  410 , one or more output devices  412 , and storage  414 . An interconnection mechanism (not shown) such as a bus, controller, or network, interconnects the components of the computer system  402 . In various embodiments of the present invention, operating system software (not shown) provides an operating environment for various softwares executing in the computer system  402 , and manages different functionalities of the components of the computer system  402 . 
     The communication channel(s)  408  allow communication over a communication medium to various other computing entities. The communication medium provides information such as program instructions, or other data in a communication media. The communication media includes, but not limited to, wired or wireless methodologies implemented with an electrical, optical, RF, infrared, acoustic, microwave, bluetooth or other transmission media. 
     The input device(s)  410  may include, but not limited to, a keyboard, mouse, pen, joystick, trackball, a voice device, a scanning device, or any another device that is capable of providing input to the computer system  402 . In an embodiment of the present invention, the input device(s)  410  may be a sound card or similar device that accepts audio input in analog or digital form. The output device(s)  412  may include, but not limited to, a user interface on CRT or LCD, printer, speaker, CD/DVD writer, or any other device that provides output from the computer system  302 . 
     The storage  414  may include, but not limited to, magnetic disks, magnetic tapes, CD-ROMs, CD-RWs, DVDs, flash drives or any other medium which can be used to store information and can be accessed by the computer system  402 . In various embodiments of the present invention, the storage  414  contains program instructions for implementing the described embodiments. 
     The present invention may suitably be embodied as a computer program product for use with the computer system  402 . The method described herein is typically implemented as a computer program product, comprising a set of program instructions which is executed by the computer system  402  or any other similar device. The set of program instructions may be a series of computer readable codes stored on a tangible medium, such as a computer readable storage medium (storage  414 ), for example, diskette, CD-ROM, ROM, flash drives or hard disk, or transmittable to the computer system  402 , via a modem or other interface device, over either a tangible medium, including but not limited to optical or analogue communications channel(s)  408 . The implementation of the invention as a computer program product may be in an intangible form using wireless techniques, including but not limited to microwave, infrared, bluetooth or other transmission techniques. These instructions can be preloaded into a system or recorded on a storage medium such as a CD-ROM, or made available for downloading over a network such as the internet or a mobile telephone network. The series of computer readable instructions may embody all or part of the functionality previously described herein. 
     The present invention may be implemented in numerous ways including as a system, a method, or a computer program product such as a computer readable storage medium or a computer network wherein programming instructions are communicated from a remote location. 
     While the exemplary embodiments of the present invention are described and illustrated herein, it will be appreciated that they are merely illustrative. It will be understood by those skilled in the art that various modifications in form and detail may be made therein without departing from or offending the spirit and scope of the invention as defined by the appended claims.