In this work, we address the problem of making decisions based on data streams, i.e., choosing an action when a new value is recorded. For instance, actions can be trading decisions in financial markets, choices of controllers in dynamic systems or perturbations of the data stream itself. To start with, we propose a language that allows individuals to formulate requirements on the action space. We use prediction techniques to identify the best possible action. However, for many scenarios there is not just one technique that predicts the future precisely, and different techniques behave quite differently. Thus, since there is no technique that dominates all the others, our conclusion is to take multiple predictions generated by different techniques into account. While ensemble techniques aggregating the predictions seem promising, existing techniques have issues, such as unnecessary information losses or the need for a predefined quality measure. Thus, we propose a new ensemble approach that weights predictions techniques according to requirements and solves an optimization problem that derives decisions directly from weighted predictions. We apply our solution to data privacy on data streams. For this setting, the benefits provided by prediction techniques have not been studied yet. In three case studies, we show that our solution consistently achieves better decision-making quality than approaches from related work.

Current mining algorithms for attributed graphs exploit dependencies between attribute information and edge structure, referred to as homophily. However, techniques fail if this assumption does not hold for the full attribute space. In multivariate spaces, some attributes have high dependency with the graph structure while others do not show any dependency. Hence, it is important to select congruent subspaces (i.e., subsets of the node attributes) showing dependencies with the graph structure. In this work, we propose a method for the statistical selection of such congruent subspaces. More specifically, we define a measure which assesses the degree of congruence between a set of attributes and the entire graph. We use it as the core of a statistical test, which congruent subspaces must pass. To illustrate its applicability to common graph mining tasks and in order to evaluate our selection scheme, we apply it to community outlier detection. Our selection of congruent subspaces enhances outlier detection by measuring outlier ness scores in selected subspaces only. Experiments on attributed graphs show that our approach outperforms traditional full space approaches and gives way to better outlier detection.