Using continuous models in scientific databases has received an increased attention in the last years. It allows for a more efficient and accurate querying, as well as predictions of the outputs even where no measurements were performed. The most common queries are on how the output looks like for a given input setting. In this paper we study inverse model-based queries on continuous models, where one specifies a desired output and searches for the appropriate input setting, which falls into the reverse engineering category. We propose two possible approaches. The first one is an extension of the inverse regression paradigm. But simply switching the roles of input and output variables poses new challenges, which we overcome by using partial least squares. The second approach formulates the inverse prediction queries as linear optimization problems. We show that even though these two approaches seem completely different, they are closely related, and that the latter is more general. It facilitates the formulation of a wide range of queries, with specifications of fixed values and ranges in both input and output space, enabling the intuitive exploration of the experimental data and understanding the underlying process.

With an ever-growing availability of data streams the interest in and need for efficient techniques dealing with such data increases. A major challenge in this context is the accurate online prediction of continuous values in the presence of concept drift. In this paper, we introduce a new adaptive model tree (AMT), designed to incrementally learn from the data stream, adapt to the changes, and to perform real time accurate predictions at anytime. To deal with sub models lying in different subspaces, we propose a new model clustering algorithm able to identify subspace models, and use it for computing splits in the input space. Compared to state of the art, our AMT allows for oblique splits, delivering more compact and accurate models.

Modeling image similarity for browsing and searching in voluminous image databases is a challenging task of nearly all content-based image retrieval systems. One promising way of defining image similarity consists in applying distance-based similarity measures on compact image representations. Beyond feature histograms and feature signatures, more general feature representations are mixture models of which the Gaussian mixture model is the most prominent one. This feature representation can be compared by employing approximations of the Kullback-Leibler Divergence. Although several of those approximations have been successfully applied to model image similarity, their applicability to mixture models based on high-dimensional feature descriptors is questionable. In this paper, we thus introduce the Signature Quadratic Form Distance to measure the distance between two Gaussian mixture models of high-dimensional feature descriptors. We show the analytical computation of the proposed Gaussian Quadratic Form Distance and evaluate its retrieval performance by making use of different benchmark image databases.

We introduce a new family of flexible feature representations for content-based multimedia retrieval: probabilistic feature signatures. While conventional feature histograms and feature signatures aggregate the multimedia objects' feature distributions exhibited in some feature space according to a partitioning, probabilistic feature signatures model these feature distributions by means of discrete or continuous probability distributions. In this way, they combine the advantages of high expressiveness and compactness, for instance through Gaussian mixture models. In this paper, we introduce the concept of probabilistic feature signatures and provide the empirical evidence of high retrieval performance when using this feature representation type. We show that probabilistic feature signatures are able to outperform conventional feature signatures.

We introduce a distance-based similarity model with application to the optimization of the fuel injection process. Our model allows for an automatic evaluation of huge and complex amount of experimental data originated from optical measurement techniques analyzing the fuel injection process. The goal is to enable researchers to get deeper insight into this process based on an automatically driven analysis.