Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last decades. The introduction of threshold-based distance functions presented a new concept of time series similarity and these functions were applied to data mining techniques on a wide spectrum of time series data. In this demonstration, we present the Java toolkit T-Time which is able to perform several data mining tasks for a complete range of threshold values in an interactive way. The results are visually presented in a very concise way so that the user can easily identify important threshold values. Combined with domain-specific knowledge, these pivotal values can yield novel insights beyond the means of the underlying data mining techniques the analysis is based on.

We present an original graph embedding to speedup distance-range andk-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph. Our method is used to compute a lower and upper bounding filter distance which approximates the true shortest path distance significantly better than traditional filters. In addition, we discuss how the computation of the exact shortest path distance in the refinement step can be boosted by using the embedded graph.

In modern multimedia databases, objects can be specified by a large variety of feature representations. In this paper, we present a novel technique for multi-represented similarity estimation. We transform the distance between two objects in each representation into so-called similarity and dissimilarity estimates which are used to derive a meaningful similarity score. To determine the parameters for our new similarity measure, we present methods with and without user feedback.

In this paper, we present an original network graph embedding to speed-up distance-range and k-nearest neighbor queries in (weighted) graphs. Our approach implements the paradigm of filter-refinement query processing and can be used for proximity queries on both static as well as dynamic objects. In particular, we present how our embedding can be used to compute a lower and upper bounding filter distance which approximates the true shortest path distance significantly better than traditional filters, e.g. the Euclidean distance. These distance approximations can be used within a filter step to prune true drops and true hits as well as in the refinement step in order to guide an informed A* search. Our experimental evaluation on several real-world data sets demonstrates a significant performance boosting of our proposed concepts over existing work.

Modeling objects by probability density functions (pdf) is a new powerful method to represent complex objects in databases. By representing an object as a pdf e.g. a Gaussian, it is possible to represent very large and complex objects in a compact and still descriptive way. In this contribution, we propose ProVeR a prototype search engine for content-based video retrieval which represents a video as a set of Gaussians. The Gaussians are managed by the Gauss-tree, an index structure allowing the efficient processing of probabilistic queries. ProVeR provides even non-expert users with an intuitive method for efficient, content-based retrieval of videos containing similar shots and scenes.

In this paper, we propose an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our method uses an approximation of the nearest-neighbor-distances in order to prune the search space. In several experiments, our solution scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.

Similarity search in time series data is an active area of research. In this paper, we introduce the novel concept of threshold-similarity queries in time series databases which report those time series exceeding a user-defined query threshold at similar time frames compared to the query time series. In addition, we present a new data structure to support threshold similarity queries efficiently. The performance of our solution is demonstrated by an extensive experimental evaluation.

Location-based services and data mining algorithms analyzing objects moving on a complex traffic network are becoming increasingly important. In this paper, we introduce a new approach which effectively and efficiently detects dense areas in spatial networks. In an offline phase, we generate a hierarchical partitioning of the traffic network. Thereby, static entities like roads and buildings are likely to be in the same partitioning if they are close to each other according to their network distance. In the online phase, our prototype ViEWNet allows the effective and efficient monitoring of objects moving on a spatial network. Based on a clear visualization of the traffic intensity in each network cell, the user can easily detect hierarchies of dense areas by our powerful prototype ViEWNet.

The rapid progress in digital music distribution has lead to the creation of large collections of music. There is a need for content-based music classification methods to organize these collections automatically using a given genre taxonomy. To provide a versatile description of the music content, several kinds of features like rhythm, pitch or timbre characteristics are commonly used. Taking the highly dynamic nature of music into account, each of these features should be calculated up to several hundreds of times per second. Thus, a piece of music is represented by a complex object given by several large sets of feature vectors. In this paper, we propose a novel approach for the hierarchical classification of music pieces into a genre taxonomy. Our approach is able to handle multiple characteristics of music content and achieves a high classification accuracy efficiently, as shown in our experiments performed on a real world data set

Similarity search in large multimedia databases is an important issue in nowadays multimedia environment. Multimedia objects such as music videos usually consist of multiple representations such as audio or video features. Since each representation may be of significantly different quality for a given multimedia object, similarity search methods could greatly benefit from taking these multiple representations into account. An intelligent similarity search technique should consider all available representations of the database objects and should automatically choose the best representations), i.e. those representations that model the object in the best possible way. In this paper, we propose a novel approach for similarity search in multimedia databases taking multiple representations of multimedia objects into account. In particular, we present weighting functions to rate the significance of a feature of each representation for a given database object. This allows weighting each representation during query processing. A broad experimental evaluation shows the suitability and the effectiveness of multi-represented similarity search in video databases

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.

The issue of data mining in time series databases is of utmost importance for many practical applications and has attracted a lot of research in the past years. In this paper, we focus on the recently proposed concept of threshold similarity which compares the time series based on the time frames within which they exceed a user-defined amplitude threshold tau. We propose a novel approach for cluster analysis of time series based on adaptable threshold similarity. The most important issue in threshold similarity is the choice of the threshold tau. Thus, the threshold tau is automatically adapted to the characteristics of a small training dataset using the concept of support vector machines. Thus, the optimal tau is learned from a small training set in order to yield an accurate clustering of the entire time series database. In our experimental evaluation we demonstrate that our cluster analysis using adaptable threshold similarity can be successfully applied to many scientific real-world data mining applications

The efficient management of complex objects has become an enabling technology for modern multimedia information systems as well as for many novel database applications. Unfortunately, the integration of modern database systems into human centered virtual reality applications including multimodal simulations fails to achieve the indispensably required interactive response times. In this paper, we present an approach which achieves efficient query processing along with industrial-strength database support for real time haptic rendering systems which compute force feedback (haptic display). Our approach externalizes and accelerates the approved main-memory Voxmap-PointShellTM (VPS) approach. We group numerous independent database queries together according to a cost model which takes statistical information reflecting the actual data distribution into account. The performance of our approach is experimentally evaluated using a realistic data-set CAR, provided by our industrial partner, a German car manufacturer. Our results show that we can achieve satisfying rendering frame rates using the presented access techniques.

Relational index structures, as for instance the Relational Interval Tree, the Relational RTree, or the Linear Quadtree, support efficient processing of queries on top of existing object-relational database systems. Furthermore, there exist effective and efficient models to estimate the selectivity and the I/O cost in order to guide the cost-based optimizer whether and how to include these index structures into the execution plan. By design, the models immediately fit to common extensible indexing/optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. In this paper, we show how these statistics can also be used for accelerating the access methods themselves by reducing the number of generated join partners. The different join partners are grouped together according to a cost-based grouping algorithm. Our first experiments on an Oracle9i database yield a speed-up of up to 1,000% for the Relational Interval Tree, the Relational R-Tree and for the Linear Quadtree.