Download Peer-to-Peer Query Processing over Multidimensional Data by Akrivi Vlachou, Christos Doulkeridis, Kjetil Nørvåg, Yannis PDF

By Akrivi Vlachou, Christos Doulkeridis, Kjetil Nørvåg, Yannis Kotidis

Applications that require a excessive measure of distribution and loosely-coupled connectivity are ubiquitous in quite a few domain names, together with clinical databases, bioinformatics, and multimedia retrieval. In these kind of purposes, info is sometimes voluminous and multidimensional, and aid for complex question operators is needed for potent querying and effective processing. to handle this problem, we undertake a hybrid P2P structure and suggest novel indexing and question processing algorithms. We current a scalable framework that depends on info summaries which are disbursed and maintained as multidimensional routing indices. types of information summaries permit effective processing of various complicated question operators.

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Sample text

Following the iDistance concept, SPA applies a clustering algorithm on 36 5 Similarity Search in Metric Spaces the list LCsp which results in a list of clusters (called hyper-clusters) LHCsp ={HCi : (Oi , ri )|1 ≤ i ≤ ksp }, where ksp the number of hyper-clusters, Oi the hyper-cluster center and ri the hyper-cluster radius, which is the distance of the farthest point of all clusters assigned to the hyper-cluster, to the centroid. Each cluster C j is mapped to a one-dimensional value based on the nearest hypercluster center Oi using formula: key j =i ∗ c + [dist(Oi , K j ) + r j ] which practically maps the farthest point of a cluster C j based on the nearest reference point Oi .

In a similar way to iDistance, peers are capable to process nearest neighbor queries locally. According to [80, 144], a peer would process the query by executing a range query with an initial range. If fewer than k data objects are retrieved, the radius of the range query is increased repeatedly, until k data objects are retrieved. Since our application area is a P2P environment, a strategy that uses a small radius and increments it until k objects are retrieved would cause more than one round-trips over the network, which is quite costly.

A range query R(q, r) can be interpreted as ”retrieve all objects that are within distance r to q”. Fig. 2(a) depicts a range query defined by query point q and a radius r. The result set of this query contains data points f and l. 2 k-Nearest Neighbor Query A drawback of range queries is that the cardinality of the result set is not known in advance, but can be anything between zero and the size of the database. Consequently, selection of an inappropriate value for the query range may lead to very few 26 4 Query Operators or too many query results.

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