During the last decade, multimedia databases have become increasingly important in many application areas such as medicine, CAD, geography, or molecular biology. An important research issue in the field of multimedia databases is the content based retrieval of similar multimedia objects such as images, text, and videos. However, in contrast to searching data in a relational database, a content based retrieval requires the search of similar objects as a basic functionality of the database system. Most of the approaches addressing similarity search use a so-called feature transformation which transforms important properties of the multimedia objects into high-dimensional points (feature vectors). Thus, the similarity search is transformed into a search of points in the feature space which are close to a given query point in the high-dimensional feature space. Query Processing in high-dimensional spaces has therefore been a very active research area over the last few years. A number of new index structures and algorithms have been proposed. It has been shown that the new index structures considerably improve the performance in querying large multimedia databases. Based on recent tutorials [BK98, BK 00], in this survey we provide an overview of the current state-of-the-art in querying multimedia databases, describing the index structures and algorithms for an efficient query processing in high-dimensional spaces. We identify the problems of processing queries in high-dimensional space, and we provide an overview of the proposed approaches to overcome these problems. Indexing Multimedia DatabasesMultimedia databases are of high importance in many application areas such as geography, CAD, medicine, or molecular biology. Depending on the application, the multimedia databases need to have different properties and need to support different types of queries. In contrast to traditional database applications, where point, range, and partial match queries are very important, multimedia databases require a search for all objects in the database which are similar (or complementary) to a given search object. In the following, we describe the notion of similarity queries and the feature-based approach to process those queries in multimedia databases in more detail.

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

In this paper, we present a new cost model for nearest neighbor search in high-dimensional data space. We first analyze different nearest neighbor algorithms, present a generalization of an algorithm which has been originally proposed for Quadtrees [13], and show that this algorithm is optimal. Then, we develop a cost model which -in contrast to previous models -takes boundary effects into account and therefore also works in high dimensions. The advantages of our model are in particular: Our model works for data sets with an arbitrary number of dimensions and an arbitrary number of data points, is applicable to different data distributions and index structures, and provides accurate estimates of the expected query execution time. To show the practical relevance and accuracy of our model, we perform a detailed analysis using synthetic and real data. The results of applying our model to Hilbert and X-tree indices show that it provides a good estimation of the query performance, which is considerably better than the estimates by previous models especially for highdimensional data.

A four-grid electrostatic energy analyzer for measurements of the ion velocity distribution and the emission of secondary electrons on the electrodes of low-pressure radio frequency glow-discharge systems has been conceived. Problems arising from poor analyzer design are discussed and the performance of the presented analyzer is shown for measurements of the ion velocity distribution in pure hydrogen, helium, and argon discharges. Moreover, the secondary electron yields on aluminium, stainless steel, copper, and amorphous silicon exposed to radio frequency argon, helium, and hydrogen plasmas are determined in situ, for the first time to our knowledge. In parallel-plate radio frequency discharges secondary electron emission involves the contributions of ions, fast neutrals, metastables, and photons impinging on the electrode surfaces. Therefore, secondary electron emission must be considered as a global phenomenon. Global secondary electron emission can be up to ten times larger than only ion-induced secondary electron emission. Typically, the global emission coefficient is of the order of 0.1 (secondary electrons per ion) but can exceed 1 when the contribution of metastables is important. This is a noteworthy result since secondary electron emission is often neglected or underestimated in modeling of electrical discharges.

In temperate Europe alley cropping systems which integrate strips of short rotation coppices into conventional agricultural fields (ACS) are receiving increasing attention. These systems can be used for crops and woody biomass production at the same time, enabling farmers to diversify the provision of market goods. Adding trees into the agricultural land creates various additional benefits for the farmer and society, also known as ecosystem services. However, tree-crop interactions in the temperate region have not been adequately substantiated which is identified as a drawback to the practical implementation of such systems. In order to bridge this gap, the current paper aims to present a comprehensive overview of selected ecosystem services provided by agroforestry with focus on ACS in the temperate region. The literature indicates that compared with conventional agriculture ACS have the potential to increase carbon sequestration, improve soil fertility and generally optimize the utilization of resources. Furthermore, due to their structural flexibility, ACS may help to regulate water quality, enhance biodiversity, and increase the overall productivity. ACS are shown as suitable land use systems especially for marginal sites. Based on the available data collected, we conclude that ACS are advantageous compared to conventional agriculture in many aspects, and therefore suggest that they should be implemented at a larger scale in temperate regions.

The detection of correlations between different features in a set of feature vectors is a very important data mining task because correlation indicates a dependency between the features or some association of cause and effect between them. This association can be arbitrarily complex, i.e. one or more features might be dependent from a combination of several other features. Well-known methods like the principal components analysis (PCA) can perfectly find correlations which are global, linear, not hidden in a set of noise vectors, and uniform, i.e. the same type of correlation is exhibited in all feature vectors. In many applications such as medical diagnosis, molecular biology, time sequences, or electronic commerce, however, correlations are not global since the dependency between features can be different in different subgroups of the set. In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Our algorithm is based on a combination of PCA and density-based clustering (DBSCAN). Our method has a determinate result and is robust against noise. A broad comparative evaluation demonstrates the superior performance of 4C over competing methods such as DBSCAN, CLIQUE and ORCLUS.

Many clustering algorithms tend to break down in high-dimensional feature spaces, because the clusters often exist only in specific subspaces (attribute subsets) of the original feature space. Therefore, the task of projected clustering (or subspace clustering) has been defined recently. As a novel solution to tackle this problem, we propose the concept of local subspace preferences, which captures the main directions of high point density. Using this concept we adopt density-based clustering to cope with high-dimensional data. In particular, we achieve the following advantages over existing approaches: Our proposed method has a determinate result, does not depend on the order of processing, is robust against noise, performs only one single scan over the database, and is linear in the number of dimensions. A broad experimental evaluation shows that our approach yields results of significantly better quality than recent work on clustering high-dimensional data.

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, is a generalization of the reverse 1-nearest neighbor problem which has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. However, existing approaches, except one, are designed for the specific R1NN problem. In addition -to the best of our knowledge -all previously proposed methods, especially the one for generalized RkNN search, are only applicable to Euclidean vector data but not for general metric objects. In this paper, we propose the first approach for efficient RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use conservative and progressive distance approximations in order to filter out true drops and true hits. In particular, we approximate the k-nearest neighbor distance for each data object by upper and lower bounds using two functions of only two parameters each. Thus, our method does not generate any considerable storage overhead. We show in a broad experimental evaluation on real-world data the scalability and the usability of our novel approach.