Decentralized multi-channel discovery system through a plurality of data structures

1. A method implemented on one or more computing devices to populate a database comprising:

initially configuring a plurality of fuse-nodes by a customizer that receives input variables and arranges one or more of categories, rankings, updates as to collected data from fuse-nodes, wherein a fuse-node includes relationship links to particular channels, media, and content, and denote different arrays of relevance to a user, wherein the initially configuring includes:

marking each of the plurality of fuse-nodes with a corresponding identifier;

setting at least one user-selected input variable, or content source, for a proprietary social recommendation algorithm to receive from and filter data for each of the plurality of fuse-nodes, wherein the algorithm is directed to a discovery process that is a request to show content from a collection of sources that are organized by fuse node;

ranking the collected data;

configuring a privacy setting of the plurality of fuse nodes, wherein the privacy setting includes a different permission model for each of the plurality of fuse-nodes; and

forming a user-selected trust filter;

storing the collected data based upon the initial configuration of the plurality of fuse-nodes;

receiving an access-request to a plurality of fuse-nodes;

determining an access-authorization of the received access-request based on a configured privacy-setting of the plurality of fuse-nodes, wherein the access-request is received from a social networking site, and determining access level authorization of a requesting user;

selecting collected data of the plurality of fuse-nodes based on the determined access-authorization; and

supplying and encrypting the selected collected data to the requesting user.

2. The method of claim 1 , wherein each data structure in the plurality of fuse-nodes is a proprietary owned database, wherein the fuse-nodes include links to other fuse-nodes, channels, media, and contents, which further include social network-friends and social network activities of the social network-friends.

3. The method of claim 1 , wherein the algorithm uses the user-selected social trust filter that limits recommended data from a social networking site, which is selected from a group of social media source.

4. The method of claim 1 , wherein the ranking of the collected data includes an assignment of a corresponding weight value to a sub-node, a channel, or a content of each of the plurality of fuse-nodes.

5. The method of claim 1 , wherein the algorithm dynamically updates ranking of the collected data based on a user-selected time period.

6. The method of claim 1 , wherein the algorithm dynamically updating of the collected data based on a fulfilment of a condition.

7. A computing device comprising:

one or more processors; and

memory accessed by the one or more processors, the memory further comprises:

a fuse-node register configured to facilitate initial configuration of a plurality of fuse-nodes, wherein the initial configuration is by a customizer that receives input variables and arranges one or more of categories, rankings, updates as to collected data from fuse-nodes wherein a fuse-node includes relationship links to particular channels, media, and content, and denote different arrays of relevance to a user, wherein the computing device receives an access request to the plurality of fuse-nodes, wherein access request is received from a social networking site, determination is made as to the received access-request based on a configured privacy-setting that includes a different permission model for each of the plurality of fuse-nodes and access level authorization of the user; selection is made as to collected data of the plurality of fuse-nodes based on the determined access-authorization, and supplying and encrypting the collected data to the user; and

supplying the selected collected data to a requesting user; wherein the plurality of nodes is created by

a local dataset that stores collected data based upon the initial configuration of the plurality of fuse-nodes; and

a fuse-node customizer configured to arrange the stored collected data, wherein the initial configuration comprise:

marking each of the plurality of fuse-nodes with a corresponding identifier;

setting at least one user-selected input variable for a proprietary social recommendation algorithm to receive from and filter data for each of the plurality of fuse-nodes, wherein the algorithm is directed to a discovery process that is a request to show content from a collection of sources that are organized by fuse node; and

configuring the privacy setting of the plurality of fuse-nodes.

8. The computing device of claim 7 , wherein each fuse-node in the plurality of fuse-nodes is a proprietary owned database that include links to other fuse-nodes, channels, media, and contents, which further include social network-friends and social network activities of the social network-friends.

9. The computing device of claim 7 , wherein the initial configuration further comprises:

ranking the collected data.

10. The computing device of claim 7 , wherein the algorithm uses a user-selected social trust filter that limits recommended data from a social networking site, which is selected from a group of social media sources.

11. The computing device of claim 7 , wherein the ranking of the collected data includes an assignment of a corresponding weight value to a channel and a content of each of the plurality of fuse-nodes.

12. The computing device of claim 7 , wherein the fuse-node customizer is further configured to dynamically update ranking of the collected data based on a user-selected time period.

13. The computing device of claim 7 , wherein the algorithm updates ranking of the collected data based on a fulfilment of a condition.

14. A non-transitory computer readable medium that comprises program instructions that causes a computer to perform the method comprising:

initially configuring a plurality of fuse-nodes by a customizer that receives input variables that arranges one or more of categories, rankings, updates as to collected data from fuse-nodes, wherein a fuse-node includes relationship links to particular channels, media, and content, and denote different arrays of relevance to a user;

storing collected data based upon the initial configuration of the plurality of fuse-nodes;

receiving an access-request to a plurality of fuse-nodes, wherein access request is received from a social networking site;

determining an access-authorization of the received access-request based on a configured privacy-setting of the plurality of fuse-nodes, wherein the privacy-setting includes a different permission model for each of the plurality of fuse-nodes, and determining access level authorization of a requesting user;

selecting collected data of the plurality of fuse-nodes based on the determined access-authorization; and

supplying and encrypting the selected collected data to the requesting user, wherein the fuse-nodes are created by:

marking each of the plurality of fuse-nodes with a corresponding identifier;

setting at least one user-selected input variable for a proprietary social recommendation algorithm to receive from and filter data for each of the plurality of fuse nodes, wherein the algorithm is directed to a discovery process that is a request to show content from a collection of sources that are organized by fuse node; and

configuring the privacy setting of the plurality of fuse-nodes.

15. The non-transitory computer readable medium of claim 14 , wherein each data structure in the plurality of fuse-nodes is a proprietary owned database that include links to other fuse-nodes, channels, media, and contents, which further include social network-friends and social network activities of the social network-friends.

16. The non-transitory computer readable medium of claim 14 , wherein creating the plurality of fuse-nodes further comprises

ranking the collected data.

17. The non-transitory computer readable medium of claim 15 , wherein the algorithm uses a user-selected social trust filter that limits recommended data from a social networking site, which is selected from a group of social media sources.