Semantic Flooding: Semantic Search across Distributed Lightweight Ontologies

Lightweight ontologies are trees where links between nodes codify the fact that a node lower in the hierarchy describes a topic (and contains documents about this topic) which is more specific than the topic of the node one level above. In turn, multiple lightweight ontologies can be connected by semantic links which represent mappings among them and which can be computed, e.g., by ontology matching. In this paper we describe how these two types of links can be used to define a semantic overlay network which can cover any number of peers and which can be flooded to perform a semantic search on documents, i.e., to perform semantic flooding. We have evaluated our approach by simulating a network of 10,000 peers containing classifications which are fragments of the DMoz web directory. The results are promising and show that, in our approach, only a relatively small number of peers needs to be queried in order to achieve high accuracy

CQ-Buddy: Harnessing Peers For Distributed Continuous Query Processing

In this paper, we present the design and evaluation of CQ-Buddy, a peer-to-peer (p2p) continuous query (CQ) processing system that is distributed, and highly-scalable. CQ-Buddy exploits the differences in capabilities (processing and memory) of peers and load-balances the tasks across powerful and weak peers. Our main contributions are as follows: First, CQ-Buddy introduces the notion of pervasive continuous queries to tackle the frequent disconnected problems common in a peer-to-peer environment. Second, CQ-Buddy allows for inter-sharing and intra-sharing in the processing of continuous queries amongst peers. Third, CQ-Buddy peers perform query-centric load balancing for overloaded data source providers by acting as proxies. We have conducted extensive studies to evaluate CQ-Buddy’s performance. Our results show that CQ-Buddy is highly scalable, and is able to process continuous queries in an effective and efficient manner.Singapore-MIT Alliance (SMA

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

Message passing on InfiniBand RDMA for parallel run-time supports

InfiniBand networks are commonly used in the high performance computing area. They offer RDMA-based operations that help to improve the performance of communication subsystems. In this paper, we propose a minimal message-passing communication layer providing the programmer with a point-to-point communication channel implemented by way of InfiniBand RDMA features. Differently from other libraries exploiting the InfiniBand features, such as the well-known Message Passing Interface (MPI), the proposed library is a communication layer only rather than a programming model, and can be easily used as building block for high-level parallel programming frameworks. Evaluated on micro-benchmarks, the proposed RDMA-based communication channel implementation achieves a comparable performance with highly optimised MPI/InfiniBand implementations. Eventually, the flexibility of the communication layer is evaluated by integrating it within the FastFlow parallel framework, currently supporting TCP/IP networks (via the ZeroMQ communication library). © 2014 IEEE

Peer Data Management

Peer Data Management (PDM) deals with the management of structured data in unstructured peer-to-peer (P2P) networks. Each peer can store data locally and define relationships between its data and the data provided by other peers. Queries posed to any of the peers are then answered by also considering the information implied by those mappings. The overall goal of PDM is to provide semantically well-founded integration and exchange of heterogeneous and distributed data sources. Unlike traditional data integration systems, peer data management systems (PDMSs) thereby allow for full autonomy of each member and need no central coordinator. The promise of such systems is to provide flexible data integration and exchange at low setup and maintenance costs. However, building such systems raises many challenges. Beside the obvious scalability problem, choosing an appropriate semantics that can deal with arbitrary, even cyclic topologies, data inconsistencies, or updates while at the same time allowing for tractable reasoning has been an area of active research in the last decade. In this survey we provide an overview of the different approaches suggested in the literature to tackle these problems, focusing on appropriate semantics for query answering and data exchange rather than on implementation specific problems

A peer to peer approach to large scale information monitoring

Issued as final reportNational Science Foundation (U.S.

Managing Linguistic Data Summaries in Advanced P2P Applications

chapitre... à corrigerAs the amount of stored data increases, data localization techniques become no longer sufficient in P2P systems. A practical approach is to rely on compact database summaries rather than raw database records, whose access is costly in large P2P systems. In this chapter, we describe a solution for managing linguistic data summaries in advanced P2P applications which are dealing with semantically rich data. The produced summaries are synthetic, multidimensional views over relational tables. The novelty of this proposal relies on the double summary exploitation in distributed P2P systems. First, as semantic indexes, they support locating relevant nodes based on their data descriptions. Second, due to their intelligibility, these summaries can be directly queried and thus approximately answer a query without the need for exploring original data. The proposed solution consists first in defining a summary model for hierarchical P2P systems. Second, appropriate algorithms for summary creation and maintenance are presented. A query processing mechanism, which relies on summary querying, is then proposed to demonstrate the benefits that might be obtained from summary exploitation