QDN

QDN at USC is "Querical Data Network", a interesting concept
It employs two approaches to solve data locating problem in a complex data system such as peer to peer network or sensor network: first is the small-world thoery, and the second is percolation theory which supports scalable flooding
http://www-scf.usc.edu/~banaeika/papers/NSF.pdf

The indexable part of QDN captures my idea on how to conduct XML queries over XML data in a P2P network. There are several good points to mention:
1. The centralized design pattern of QDN is actually data shipping, while the decentralized design is query shipping. The eciency of the decentralized design is due to in-network processing.
2. Measurement metrics include precision, recall ratio, and hop count
3. , we argue that as a model DHT fails to respect natural characteristics/requirements of
QDNs. For example, data-to-node assignment via the virtual identifier space
violates the natural distribution and replication of the data, where each node
autonomously maintains its own and only its own data. Also, regular topology
of the DHT imposes strict connectivity rules to autonomous nodes.
4. Represent each node as multiple virtual nodes by taking each tuple tk as a virtual identity, which increases the size of the QDN, but it is more accurate.
5. with wooding the node that receives the first hit during selective walk, marks
the query for scope-limited ooding and continues forwarding the query by originating the wooding.
6. . Percolation theory analyzes
the statistical and geometrical properties of such clusters as the probability p
changes. Statistical distribution of the cluster size or cluster mass, i.e., number
of sites within the cluster, cluster surface, i.e., length of the cluster perimeter,
and cluster shape, i.e., fractal geometry of the cluster boundaries, are among the
properties of interest in the percolation theory.