74,252 research outputs found
Semantic Query Reformulation in Social PDMS
We consider social peer-to-peer data management systems (PDMS), where each
peer maintains both semantic mappings between its schema and some
acquaintances, and social links with peer friends. In this context,
reformulating a query from a peer's schema into other peer's schemas is a hard
problem, as it may generate as many rewritings as the set of mappings from that
peer to the outside and transitively on, by eventually traversing the entire
network. However, not all the obtained rewritings are relevant to a given
query. In this paper, we address this problem by inspecting semantic mappings
and social links to find only relevant rewritings. We propose a new notion of
'relevance' of a query with respect to a mapping, and, based on this notion, a
new semantic query reformulation approach for social PDMS, which achieves great
accuracy and flexibility. To find rapidly the most interesting mappings, we
combine several techniques: (i) social links are expressed as FOAF (Friend of a
Friend) links to characterize peer's friendship and compact mapping summaries
are used to obtain mapping descriptions; (ii) local semantic views are special
views that contain information about external mappings; and (iii) gossiping
techniques improve the search of relevant mappings. Our experimental
evaluation, based on a prototype on top of PeerSim and a simulated network
demonstrate that our solution yields greater recall, compared to traditional
query translation approaches proposed in the literature.Comment: 29 pages, 8 figures, query rewriting in PDM
Decision Support for Distributed Database Fragmentation and Allocation Schema Design
If peer to peer distributed database systems meet current expectations, they are likely to replace virtually all centralized database systems over the next decade. One impediment to the proliferation of peer to peer distributed database systems is the lack of proven and established normative methodologies for designing distributed database fragmentation and allocation schemas. The literature discussed here serves as the basis of research-in-progress for designing, implementing, and empirically evaluating a support system to aid distributed database fragmentation and allocation schema decision making. Future manuscripts are planned that describe the prototype decision support system and our empirically-based experiences
An Efficient Architecture for Information Retrieval in P2P Context Using Hypergraph
Peer-to-peer (P2P) Data-sharing systems now generate a significant portion of
Internet traffic. P2P systems have emerged as an accepted way to share enormous
volumes of data. Needs for widely distributed information systems supporting
virtual organizations have given rise to a new category of P2P systems called
schema-based. In such systems each peer is a database management system in
itself, ex-posing its own schema. In such a setting, the main objective is the
efficient search across peer databases by processing each incoming query
without overly consuming bandwidth. The usability of these systems depends on
successful techniques to find and retrieve data; however, efficient and
effective routing of content-based queries is an emerging problem in P2P
networks. This work was attended as an attempt to motivate the use of mining
algorithms in the P2P context may improve the significantly the efficiency of
such methods. Our proposed method based respectively on combination of
clustering with hypergraphs. We use ECCLAT to build approximate clustering and
discovering meaningful clusters with slight overlapping. We use an algorithm
MTMINER to extract all minimal transversals of a hypergraph (clusters) for
query routing. The set of clusters improves the robustness in queries routing
mechanism and scalability in P2P Network. We compare the performance of our
method with the baseline one considering the queries routing problem. Our
experimental results prove that our proposed methods generate impressive levels
of performance and scalability with with respect to important criteria such as
response time, precision and recall.Comment: 2o pages, 8 figure
PeerDB-Peering into Personal Databases
In this talk, we will present the design and evaluation of PeerDB, a peer-to-peer (P2P) distributed data sharing system. PeerDB distinguishes itself from existing P2P systems in several ways. First, it is a full-fledge data management system that supports fine-grain content-based searching. Second, it facilitates sharing of data without shared schema. Third, it combines the power of mobile agents into P2P systems to perform operations at peers' sites. Fourth, PeerDB network is self-configurable, i.e., a node can dynamically optimize the set of peers that it can communicate directly with based on some optimization criterion.Singapore-MIT Alliance (SMA
Mobile Peer-to-Peer
Peer-to-peer systems are gaining increasing popularity as a scalable means to
share data among a large number of autonomous nodes. Since the shared data are
unstructured and they do not follow a global schema, XML-based descriptions
of the data can be used to provide a uniform way to query the heterogeneous
data. In our research, we are interested in designing a fully decentralized approach for the problem of espciently routing path queries among the nodes of
a peer-to-peer system. Our approach is based on (a) selecting and maintaining
specialized data structures, called filters that efficiently summarize the content,
i.e., the documents, of one or more node and (b) using these filters to build
an overlay network that groups together nodes with similar content
PicShark: mitigating metadata scarcity through large-scale P2P collaboration
With the commoditization of digital devices, personal information and media sharing is becoming a key application on the pervasive Web. In such a context, data annotation rather than data production is the main bottleneck. Metadata scarcity represents a major obstacle preventing efficient information processing in large and heterogeneous communities. However, social communities also open the door to new possibilities for addressing local metadata scarcity by taking advantage of global collections of resources. We propose to tackle the lack of metadata in large-scale distributed systems through a collaborative process leveraging on both content and metadata. We develop a community-based and self-organizing system called PicShark in which information entropy—in terms of missing metadata—is gradually alleviated through decentralized instance and schema matching. Our approach focuses on semi-structured metadata and confines computationally expensive operations to the edge of the network, while keeping distributed operations as simple as possible to ensure scalability. PicShark builds on structured Peer-to-Peer networks for distributed look-up operations, but extends the application of self-organization principles to the propagation of metadata and the creation of schema mappings. We demonstrate the practical applicability of our method in an image sharing scenario and provide experimental evidences illustrating the validity of our approac
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