A survey on self‐organized semantic storage <s> S-RDF <s> Decentralized and unstructured peer-to-peer networks such as Gnutella are attractive for certain applications because they require no centralized directories and no precise control over network topology or data placement. However, the flooding-based query algorithm used in Gnutella does not scale; each query generates a large amount of traffic and large systems quickly become overwhelmed by the query-induced load. This paper explores, through simulation, various alternatives to Gnutella's query algorithm, data replication strategy, and network topology. We propose a query algorithm based on multiple random walks that resolves queries almost as quickly as Gnutella's flooding method while reducing the network traffic by two orders of magnitude in many cases. We also present simulation results on a distributed replication strategy proposed in [8]. Finally, we find that among the various network topologies we consider, uniform random graphs yield the best performance. <s> BIB001 </s> A survey on self‐organized semantic storage <s> S-RDF <s> Napster pioneered the idea of peer-to-peer file sharing, and supported it with a centralized file search facility. Subsequent P2P systems like Gnutella adopted decentralized search algorithms. However, Gnutella's notoriously poor scaling led some to propose distributed hash table solutions to the wide-area file search problem. Contrary to that trend, we advocate retaining Gnutella's simplicity while proposing new mechanisms that greatly improve its scalability. Building upon prior research [1, 12, 22], we propose several modifications to Gnutella's design that dynamically adapt the overlay topology and the search algorithms in order to accommodate the natural heterogeneity present in most peer-to-peer systems. We test our design through simulations and the results show three to five orders of magnitude improvement in total system capacity. We also report on a prototype implementation and its deployment on a testbed. <s> BIB002 </s> A survey on self‐organized semantic storage <s> S-RDF <s> Built specifically for the Semantic Web, triple stores are required to accommodate a large number of RDF triples and remain primarily centralized. As triple stores grow and evolve with time, there is a demanding need for scalable techniques to remove resource and performance bottlenecks in such systems. To this end, we propose a fully decentralized peer-to-peer architecture for large scale triple stores in which triples are maintained by individual stakeholders, and a semantics-directed search protocol, mediated by topology reorganization, for locating triples of interest. We test our design through simulations and the results show anticipated improvements over existing techniques for distributed triple stores. In addition to engineering future large scale triple stores, our work will in particular benefit the federation of stand-alone triple stores of today to achieve desired scalability. <s> BIB003
S-RDF is a fully distributed solution aimed solely at the discovery of RDF resources over a large unstructured network BIB003 . The authors discard the possibility of using a structured P2P network due to the high impact the reorganization of overlay network structures has in quickly changing networks BIB002 . The approach chosen is the so-called biased multiple random walk BIB001 , where a configurable number of search requests is started on the node searching for data. Search requests are then routed from node to node using a list of known "neighbor" nodes advertised by an out-of-band mechanism, and where routing decisions are not based on a global law, but rather on independent decisions of the participating nodes. This routing decision is inherently random, thus retrieval operations might require an undue amount of hops inside the network before finding relevant data. To mend this issue, the randomness of the routing decision is biased according to meta data about RDF data stored on the neighboring nodes. The RDF data to be published are analyzed by first finding all resources and their associated type(s). From these types, the most general type is selected and used to describe the particular set of RDF data. These types are then transmitted to neighboring nodes, where they bias routing decisions for subjects of matching or derived types using an ontology-based similarity function. Furthermore, based on the percentage of times an incoming query was answered successfully, each node decides if a topology reorganization is needed. If so, another out-of-band mechanism is used to include peers storing data related to those locally stored into the neighbor list. The approach to aggregate meta data and use this information in query routing relies on the presence of ontology information. In some cases, those can be extracted from the stored data, but only on the nodes actually storing the relevant data set. Other nodes are not able to determine the type structure without being given the ontology definition. Also, the mechanism to reorganize the network topology towards grouping peers hosting semantically related data sets together is also based on a globally consistent ontology structure, which may not exist. Also, generalization of defining types from RDF data may require manual interaction. An algorithm may be forced to summarize every file to the type owl:Thing, a class to which every RDF resource belongs to by definition.