introduce a novel and flexible index structure for multidimensional data, the SH-tree (Super Hybrid tree). Theoretical analyses show that the SH-tree is a good combination of both techniques with respect to both presentation and search algorithms. It overcomes the shortcomings and makes use

more than one index technique, the number of techniques that are derived from these two main classes is just a few. In this paper, we introduce such a “mixed” index, the SH-tree: a novel and flexible super hybrid index structure for multidimensional data. Theoretical analyses indicate that the SH-tree

Coming along with the development of modern database applications, various index structures have been created to support the efficient storage and retrieval of data. These index structures include multidimensional access methods (MAMs), which take a fundamental and vital role in modern database

We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing information-embedding rate

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■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery

The proliferation of applications that must reliably distribute bulk data to a large number of autonomous clients motivates the design of new multicast and broadcast prot.ocols. We describe an ideal, fully scalable protocol for these applications that we call a digital fountain. A digital fountain

to the query object. In this paper, we introduce two adapted algorithms to the SH-tree [KKW2001] from the state-of-the-art of corresponding research results for efficiently processing the nearest neighbor problem in spatial databases. We do intensive performance tests on synthetic data sets, which dimension

. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter

evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure, but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when