Extracting knowledge from data is, in most cases, not restricted to the analysis itself but accompanied by preparation and post-processing steps. Handling data coming directly from the source, e. g. a sensor, often requires preconditioning like parsing and removing irrelevant information before data mining algorithms can be applied to analyze the data. Stand-alone data mining frameworks in general do not provide such components since they require a specified input data format. Furthermore, they are often restricted to the available algorithms or a rapid integration of new algorithms for the purpose of quick testing is not possible. To address this shortcoming, we present the data analysis framework Knowing, which is easily extendible with additional algorithms by using an OSGi compliant architecture. In this demonstration, we apply the Knowing framework to a medical monitoring system recording physical activity. We use the data of 3D accelerometers to detect activities and perform data mining techniques and motion detection to classify and evaluate the quality and amount of physical activities. In the presented use case, patients and physicians can analyze the daily activity processes and perform long term data analysis by using an aggregated view of the results of the data mining process. Developers can integrate and evaluate newly developed algorithms and methods for data mining on the recorded database.

Low depth of field (DOF) is an important technique to emphasize the object of interest (OOI) within an image. When viewing a low depth of field image, the viewer implicitly segments the image into region of interest and non regions of interest which has major impact on the perception of the image. Thus, robust algorithms for the detection of the OOI in low DOF images provide valuable information for subsequent image processing and image retrieval. In this paper we propose a robust and parameterless algorithm for the fully automatic segmentation of low depth of field images. We compare our method with three similar methods and show the superior robustness even though our algorithm does not require any parameters to be set by hand. The experiments are conducted on a real world data set with high and low depth of field images.

Similarity queries are an important query type in multimedia databases. To implement these types of queries, database systems often use spatial index structures like the R*-Tree. However, the majority of performance evaluations for spatial index structures rely on a conventional background storage layer based on conventional hard drives. Since newer devices like solid-state-disks (SSD) have a completely different performance characteristic, it is an interesting question how far existing index structures profit from these modern storage devices. In this paper, we therefore examine the performance behaviour of the R*-Tree on an SSD compared to a conventional hard drive. Testing various influencing factors like system load, dimensionality and page size of the index our evaluation leads to interesting insights into the performance of spatial index structures on modern background storage layers.

When searching for optimal paths in a network, algorithms like A*-search need an approximation of the minimal costs between the current node and a target node. A reference node embedding is a universal method for making such an approximation working for any type of positive edge weights. A drawback of the approach is that the memory consumption of the embedding is linearly increasing with the number of attributes and landmarks. In this paper, we propose methods for significantly decreasing the memory consumption of embedded graphs and examine the impact of the landmark selection.

Modern maps provide a variety of information about roads and their surrounding landscape allowing navigation systems to go beyond simple shortest path computation. In this demo, we show how the concept of skyline queries can be successfully adapted to routing problems considering multiple road attributes. In particular, we demonstrate how to compute several pareto-optimal paths which contain optimal results for a variety of user preferences. The PAROS-system has two main purposes. The first is to calculate the route skyline for a starting point and a destination. Our demonstrator visualizes the result set for up to three road attributes. Therefore, we provide a dual view on the computed skyline paths. The first view displays the result paths on the road map itself. The second view describes the result paths in the property space, displaying the trade-off between the underlying criteria. Thus, a user can browse through the results in order to find the path which fits best to his personal preferences. The second component of our system suits analysis issues. In this component, we illustrate the functionality of the underlying route skyline algorithm. Thus, we provide benchmark information about processing time and the search space visited during route skyline computation.