We introduce collaborative tagging and faceted search on structured P2P systems. Since a trivial and brute force mapping of an entire folksonomy over a DHT-based system may reduce scalability, we propose an approximated graph maintenance approach. Evaluations on real data coming from Last.fm prove that such strategies reduce vocabulary noise (i.e., representation's overfitting phenomena) and hotspots issues.
PINTS comes as a building block of Tagster @cite_12 , a distributed content sharing and tagging system where the user-resource-tag graph is stored in a DHT. A dedicated storing index is used for each tagging relation, so each edge in the graph is stored at different overlay responsibility areas. For this reason, one lookup for each edge retrieval is needed, and this could make the navigation expensive in systems with a huge number of tags and objects. Furthermore, navigational aspects between related tags is not explicitly taken into account.
@cite_12
2026312584 1587208850 2151682391 2078425114
Emerging social network applications for sharing and collaboration need a way to publish and search a big amount of social objects. Currently, social applications allow the association of a set of user defined keywords, named tags, when publishing these objects, in order to allow searching for them later using a subset of these tags. Commercial systems and recent research community proposals preclude a wide Internet deployment due to the emergence of scalability and hot spot problems in the nodes. We propose T-DHT, an innovative hybrid unstructured-structured DHT based approach, to cope with these high demanding requirements, in a fully scalable, distributed and balanced way. The storage process allows attaching a set of user tags to the stored object and takes at most O(Log(N)) node hops. The tag information attached to the object is stored in a compact way into the node links using a bloom filter, in order to be used later in the search process. The search process allows searching for previously stored objects by means of a tag conjunction and also takes at most O(Log(N)) node hops. The search process is based on DHT typical search combined with an unstructured search algorithm using the tag information previously stored into bloom filters of node links. The simulation results show T-DHT performs in a fully balanced and scalable way, without generating typical hot spot problems even if unbalanced distributions of popular tags are used. Although T-DHT has been devised to build a scalable infrastructure for social applications, it can be applied to solve the more general Peer-to-Peer keyword search problems. Distributed Hash Tables (DHTs) are very efficient distributed systems for routing, but at the same time vulnerable to disruptive nodes. Designers of such systems want them used in open networks, where an adversary can perform a sybil attack by introducing a large number of corrupt nodes in the network, considerably degrading its performance. We introduce a routing strategy that alleviates some of the effects of such an attack by making sure that lookups are performed using a diverse set of nodes. This ensures that at least some of the nodes queried are good, and hence the search makes forward progress. This strategy makes use of latent social information present in the introduction graph of the network. Mobile ad-hoc networks (MANETs) and distributed hash-tables (DHTs) share key characteristics in terms of self organization, decentralization, redundancy requirements, and limited infrastructure. However, node mobility and the continually changing physical topology pose a special challenge to scalability and the design of a DHT for mobile ad-hoc network. The mobile hash-table (MHT) [9] addresses this challenge by mapping a data item to a path through the environment. In contrast to existing DHTs, MHT does not to maintain routing tables and thereby can be used in networks with highly dynamic topologies. Thus, in mobile environments it stores data items with low maintenance overhead on the moving nodes and allows the MHT to scale up to several ten thousands of nodes.This paper addresses the problem of churn in mobile hash tables. Similar to Internet based peer-to-peer systems a deployed mobile hash table suffers from suddenly leaving nodes and the need to recover lost data items. We evaluate how redundancy and recovery technique used in the internet domain can be deployed in the mobile hash table. Furthermore, we show that these redundancy techniques can greatly benefit from the local broadcast properties of typical mobile ad-hoc networks. In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment.