An amateur's introduction to recursive query processing strategies

We propose a novel multi-dimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multi-dimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in non content dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.

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Paving the Way to an Effective and Efficient Retrieval of Data over Semantic Overlay Networks

The Semantic Web for Knowledge and Data Management ◽

10.4018/978-1-60566-028-8.ch007 ◽

2010 ◽

pp. 151-175

Author(s):

Federica Mandreoli ◽

Riccardo Martoglia ◽

Wilma Penzo ◽

Simona Sassatelli ◽

Giorgio Villani

Keyword(s):

Query Processing ◽

Overlay Networks ◽

Local Data ◽

System A ◽

Processing Strategies ◽

Efficient Retrieval ◽

Common Interests ◽

Routing Mechanism ◽

Indexing Mechanism ◽

P2p System

In a Peer-to-Peer (P2P) system, a Semantic Overlay Network (SON) models a network of peers whose connections are influenced by the peers’ content, so that semantically related peers connect with each other. This is very common in P2P communities, where peers share common interests, and a peer can belong to more than one SON, depending on its own interests. Querying such a kind of systems is not an easy task: The retrieval of relevant data can not rely on flooding approaches which forward a query to the overall network. A way of selecting which peers are more likely to provide relevant answers is necessary to support more efficient and effective query processing strategies. This chapter presents a semantic infrastructure for routing queries effectively in a network of SONs. Peers are semantically rich, in that peers’ content is modelled with a schema on their local data, and peers are related each other through semantic mappings defined between their own schemas. A query is routed through the network by means of a sequence of reformulations, according to the semantic mappings encountered in the routing path. As reformulations may lead to semantic approximations, we define a fully distributed indexing mechanism which summarizes the semantics underlying whole subnetworks, in order to be able to locate the semantically best directions to forward a query to. In support of our proposal, we demonstrate through a rich set of experiments that our routing mechanism overtakes algorithms which are usually limited to the only knowledge of the peers directly connected to the querying peer, and that our approach is particularly successful in a SONs scenario.

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