A survey on self‐organized semantic storage <s> RDFPeers <s> A fundamental problem that confronts peer-to-peer applications is to efficiently locate the node that stores a particular data item. This paper presents Chord, a distributed lookup protocol that addresses this problem. Chord provides support for just one operation: given a key, it maps the key onto a node. Data location can be easily implemented on top of Chord by associating a key with each data item, and storing the key/data item pair at the node to which the key maps. Chord adapts efficiently as nodes join and leave the system, and can answer queries even if the system is continuously changing. Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes. <s> BIB001 </s> A survey on self‐organized semantic storage <s> RDFPeers <s> The Resource Description Framework provides a powerful model for structured knowledge representation that allows the inference of new knowledge. Because of the anticipated scope of semantic information available in the future, centralized databases will become incapable of handling the load. Peer-to-Peer based distributed databases offer better scalability and integration of many different data sources. In this paper we present a detailed data management strategy for a DHT based RDF store that provides reasoning, robustness, and load-balancing. <s> BIB002
RDFPeers is a P2P network of nodes storing RDF triples . In order to be able to resolve queries, a DHT is used to locate the nodes within the network responsible for storing a particular RDF triple. RDFPeers is based on the Chord network structure, where the overlay network is a modulo-ring BIB001 . Here, each data item being stored is hashed using a globally known hash function, and each node is responsible for a particular range of the hash functions' result space. Each node also keeps pointers to the nodes responsible for the result space ranges following its own range -the so-called successor table -as well as pointers to other parts of the hash ring -the finger table -with exponentially increasing step size. Replication is performed by also storing new data items on a configurable number of nodes in the successor table. An overlay network using a ring structure is shown in Figure 3 . Here, each node maintains a connection to at least one successor node in the ring. Requests sent to any node can be forwarded to the node responsible for the respective key range, for example a request for the key 1.1 sent to node S6 would be routed to S2 over S1. Chord has been extended for the "Multi-Attribute Addressable Network for Grid Information Services" , which is the direct basis of RDFPeers. In MAAN, three important changes are added to Chord: first, range queries become possible by using an order-preserving hash function. Second, the single-key limitation inherent in DHTs is extended to support multiple attributes for each stored data item. This is achieved by storing each item multiple times, each time with a different attribute as the DHT key. Query resolution is now performed by starting the query on one attribute Self-organized semantic storage while carrying the other restrictions as well, thereby achieving logarithmic performance on retrieval, with the storage performance growing linearly on the number of attributes. Third, load balancing, is achieved by not only using a locality preserving hash function, but a uniform locality preserving hash function. However, in order to calculate such a function, the statistical distribution function of the keys to be expected has to be known in advance. Even though RDFPeers has been prudently designed to be scalable and to be able to answer almost all queries as well as store data with logarithmic effort with regards to the number of nodes to be visited, various issues are apparent: in order to create a uniform locality preserving hash function as required by MAAN to achieve an even storage load distribution, an analysis of the RDF graph to be stored is required. This manual interaction is undesirable. Skewed data popularity within queries is also not handled, and the hot spot issue for popular resources or predicates also applies BIB002 . Robustness is also seriously limited by relying on the ring overlay network structure, which requires costly reconstruction including movement should nodes fail or join the network.