P2P file sharing networks allowing participants to freely assign structured meta-data to files

The present paper proposes the concept of peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured meta-data to files. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured meta-data for file search. Vectorized Kansei information as meta-data indicates what participants feel to their own files and is assigned by the participant to each of their own files. Therefore, vectorized Kansei information is a sort of structured meta-data that people can freely assign to files. A search query also has the same form of vectorized Kansei information and indicates what participants want to feel to files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query from a peer making the query to peers that are likely to hold more files having meta-data that is similar to the query. The similarity between the search query and the meta-data is measured in terms of their dot product. From the viewpoint of P2P file sharing, in which all of the peers are equal in terms of function, it is not good for certain peers to have an advantage in P2P file search due to their Kansei information. Therefore, the simulation experiments herein examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which the Kansei information of the peers and the tendency of the peers with respect to file collection are diverse. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary.