Peer-to-peer indexing-based marketplace

A system for classifying data comprising:

• a computing device comprising a memory and a processor, the computing device storing on the memory for execution by the processor:
• a higher order indexing module configured to:
• (a) perform higher-order indexing to generate a plurality of topic indexes from a pre-selected data set, the higher-order indexing based on relationships between information in the pre-selected data set;
• (b) compute a lift metric on said topic indexes to gauge information content of said topic indexes;
• (c) compute similarity of said topic indexes;
• and
• (d) classify data received by said higher order indexing module into a topic index in said topic indexes based on the relationships between the information in the pre-selected data set,
• wherein the higher-order indexing module calculates probabilities:
• P_(t|C)=(# of higher-order paths in class C comprising entity t)/(# of higher-order paths in class C) and
• P(C)=(# of higher-order paths in class C)/(total # of higher-order paths), and
• wherein the higher-order indexing module defines higher-order paths using a non-empty graph G=(V,E) of the form V={x0, x1, . . . xk}, E={(x0, x1, x2, . . . , xk-1xk} with nodes xi distinct, two vertices xi and xk linked by path P where the number of edges in P is its length, where vertices V={e0, e1, . . . , ek} represent entities, and edges E={r0, r1, . . . , rm} represent records, documents, vectors, or instances, and wherein both vertices and edges are distinct.

A method for supporting an electronic marketplace for the buying, selling, and general exchange of indexed information, comprising the steps of:

• (a) determining, by a first computing device, a data set;
• (b) indexing, by the first computing device, said data set using higher-order methods to create a plurality of topic indexes;
• (c) generating, by the first computing device, a lift metric of said topic indexes to measure information quality of said topic indexes;
• (d) assigning, by the first computing device, a market valuation to each of said topic indexes based on said lift metric;
• (e) exchanging, by the first computing device, a plurality of said market valuations with a plurality of other computing devices over a network;
• (f) transmitting, by the first computing device, said plurality of topic indexes to at least a second computing device in the plurality of computing devices;
• and
• (g) after the transmitting, receiving, by the first computing device from the at least a second computing device over the network, one or more of items, currencies, services, and terms of market value,
• wherein the indexing of said data set using higher-order methods further comprises calculating probabilities:
• P_(t|C)=(# of higher-order paths in class C comprising entity t)/(# of higher-order paths in class C) and
• P(C)=(# of higher-order paths in class C)/(total # of higher-order paths)

A method for supporting an electronic marketplace for the buying, selling, and general exchange of indexed information, comprising the steps of:

• (a) determining, by a first computing device, a data set;
• (b) indexing, by the first computing device, said data set using higher-order methods to create a plurality of topic indexes;
• (c) generating, by the first computing device, a lift metric of said topic indexes to measure information quality of said topic indexes;
• (d) assigning, by the first computing device, a market valuation to each of said topic indexes based on said lift metric;
• (e) exchanging, by the first computing device, a plurality of said market valuations with a plurality of other computing devices over a network;
• (f) transmitting, by the first computing device, said plurality of topic indexes to at least a second computing device in the plurality of computing devices;
• and
• (g) after the transmitting, receiving, by the first computing device from the at least a second computing device over the network, one or more of items, currencies, services, and terms of market value,
• wherein the indexing of said data set using higher-order methods further comprises calculating probabilities:
• P_(t|C)=(# of higher-order paths in class C comprising entity t)/(# of higher-order paths in class C) and
• P(C)=(# of higher-order paths in class C)/(total # of higher-order paths)
• wherein the higher-order paths are defined using a non-empty graph G=(V,E) of the form V={x0, x1, . . . , xk}, E={(x0, x1, x2, . . . , xk-1xk} with nodes xi distinct, two vertices xi and xk linked by path P where the number of edges in P is its length, where vertices V={e0, e1, . . . , ek} represent entities, and edges E={r0, r1, . . . , rm} represent records, documents, vectors, or instances, and wherein both vertices and edges are distinct.