By Philippe Cudre-Mauroux
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Wk ) pondering the importance of the attributes as: SIMσ q, (µn ◦ · · · ◦ µ1 )(q) = W · FV σ W 1k where W · FV σ = W1 FVσ1 + · · · + Wk FVσk and X = X 2 = x21 + · · · + x2k and 1k is a k-dimensional unit vector. This value is normalized on the interval [0, 1]. Originally, the similarity is one, and it decreases proportionally to the relative weight and selectivity of every attribute lost in the selection predicates. For projections, analogous similarity measures SIMπ are derived based on feature vectors FV π .
Those systems provide hash-table functionalities on an Internet-like scale, and are today known as Distributed Hash-Tables (DHTs). They typically, find a data item in a totally decentralized way in O log(N ) messages, where N is the number of peers in the system. e. that some peers can provide more resources than others in a P2P system, several systems (most notably Kazaa [LKR06]) adopted a two-layered P2P structure called a super-peer [YGM03] architecture. 2(ii) shows a super-peer overlay network where peers are organized in a typical two-tier hierarchy: simple peers connect to one of the four superpeers, which supposedly enjoy a greater stability, superior bandwidth or CPU capabilities.
In SQL, XQuery or SPARQL. For a peer p structuring its data according to a schema Sp , the following operators are considered: • Projection πpa , where pa is a list of attribute names (A1 , . , Ak ) ∈ S p . • Selection σpred (sa) , where sa is a list of attributes (A1 , . . g. σpred (sa) = σAi