Abstract: Disclosed is a system for determining an expert of one or more subjects on a web-based platform. The system comprises a mining module for mining activity data of at least one user of a plurality of users from the web-based platform. The mining module may further compare the activity data with one or more subjects. The mining module may further label the activity data to a subject of the one or more subjects. A scoring module may assign performance points to the at least one user associated to the activity data. The scoring module may further assign subject points to the subject. The scoring module may further generate an activity gauge for the at least one user based on the performance points assigned and the subject points. The scoring module may further classify the at least one user as the expert of the subject.

Abstract: Disclosed is a method and system for analyzing a social network, the method comprising: segregating, by a processor, the social network into a plurality of groups of users, wherein each group of users has a unique identity in the social network; mining, by the processor, data from a group of users of the plurality of groups of users; identifying a plurality of social variables from the data, wherein the plurality of social variables comprises at least one of a number of users, connections of the users, interactions of the users, affinity of the users, or posts by the users; equating a social variable of the plurality of social variables with a thermodynamic variable of a plurality of thermodynamic variables; determining quantitative values of the plurality of thermodynamic variables based upon the plurality of social variables; generating a virtual thermodynamics system based upon the quantitative values for analyzing the social network.

Abstract: Disclosed herein is a web-based learning apparatus and a method for progressive learning. The apparatus and a method enables identifying a learning flow pattern of the user for a particular topic by mapping the synchronicity of events with interest graphs in similarity sets. Further, the shift in user learning pattern and interest graph is recorded to enable the system derive an updated learning matrix of the user that is capable of presenting and recommending to the user of new expanded matrix comprising topics of his evolving interest.

Abstract: Disclosed is a system for determining an expert of one or more subjects on a web-based platform. The system comprises a mining module for mining activity data of at least one user of a plurality of users from the web-based platform. The mining module may further compare the activity data with one or more subjects. The mining module may further label the activity data to a subject of the one or more subjects. A scoring module may assign performance points to the at least one user associated to the activity data. The scoring module may further assign subject points to the subject. The scoring module may further generate an activity gauge for the at least one user based on the performance points assigned and the subject points. The scoring module may further classify the at least one user as the expert of the subject.

Abstract: Disclosed is a method and system for analyzing a social network, the method comprising: segregating, by a processor, the social network into a plurality of groups of users, wherein each group of users has a unique identity in the social network; mining, by the processor, data from a group of users of the plurality of groups of users; identifying a plurality of social variables from the data, wherein the plurality of social variables comprises at least one of a number of users, connections of the users, interactions of the users, affinity of the users, or posts by the users; equating a social variable of the plurality of social variables with a thermodynamic variable of a plurality of thermodynamic variables; determining quantitative values of the plurality of thermodynamic variables based upon the plurality of social variables; generating a virtual thermodynamics system based upon the quantitative values for analyzing the social network.

Abstract: Systems and methods for developing a facet data network for an employee of an enterprise. The method comprises receiving a plurality of information records from at least one information source. The plurality of information records relates to at least one activity performed by the employee of the enterprise. The method further comprises associating each of the plurality of information records with at least one facet from amongst a plurality of facets defined for one or more employees of the enterprise. The method further comprises generating a plurality of information packets, each information packet corresponding to an information record from amongst the plurality of information records. An information packet from amongst the plurality of information packets links to one or more other information packets from amongst the plurality of information packets for building the facet data network.