Titlebook: Scientific and Statistical Database Management; 20th International C Bertram Ludäscher,Nikos Mamoulis Conference proceedings 2008 Springer-

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COUCH

Query Selectivity Estimation for Uncertain Dataabases to include uncertain (probabilistic) data as a native data type. New automatic query optimizations having the ability to estimate the cost of execution of a given query plan, as available in existing databases, need to be developed. For probabilistic data this involves providing selectivity e

miniature

Disclosure Risks of Distance Preserving Data Transformationsating the privacy of individuals?” is still on the table, and research is being conducted to find efficient methods to do that. Data transformation was previously proposed as one efficient method for privacy preserving data mining when a party needs to out-source the data mining task, or when distri

burnish

EWE

A Probabilistic Framework for Building Privacy-Preserving Synopses of Multi-dimensional Datad in the last three decades. Several summarization techniques have been proposed, based on different approaches, such as histograms, wavelets and sampling. The aim of most of the works in this area was to devise techniques for constructing effective synopses, enabling range queries to be estimated,

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热情赞扬

Hierarchical Graph Embedding for Efficient Query Processing in Very Large Traffic Networksgraph that is applicable also for very large networks. Our method extends an existing embedding called reference node embedding which can be used to compute accurate lower and upper bounding filters for the true shortest path distance. In order to solve the problem of high storage cost for the netwo

ANA

Monitoring Aggregate k-NN Objects in Road Networks .-NN objects from a single query point over a road network, or computing the snapshot .-NN objects over a road network to minimize an aggregate distance function with respect to multiple query points. In this paper, we study a new problem that is to monitor .-NN objects over a road network from mul

SEVER

finale

Efficiently Discovering Recent Frequent Items in Data Streams, and several techniques have been proposed for its solution, these approaches treat all the values of the data stream equally. Nevertheless, not all values are of equal importance. In several situations, we are interested more in the new values that have appeared in the stream, rather than in the o