Conny Junghans

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Sensor networks play a central role in applications that monitor variables in geographic areas such as the traffic volume on roads or the temperature in the environment. A key feature users are often interested in when employing such systems is the detection of unusual phenomena, that is, anomalous values measured by the sensors. In this demonstration, we(More)
Sensor networks play an important role in applications concerned with environmental monitoring, disaster management , and policy making. Effective and flexible techniques are needed to explore unusual environmental phenomena in sensor readings that are continuously streamed to applications. In this paper, we propose a framework that allows to detect outlier(More)
Document similarity measures play an important role in many document retrieval and exploration tasks. Over the past decades, several models and techniques have been developed to determine a ranked list of documents similar to a given query document. Interestingly, the proposed approaches typically rely on extensions to the vector space model and are rarely(More)
Due to the inherent characteristics of data streams, appropriate mining techniques heavily rely on window-based processing and/or (approximating) data summaries. Because resources such as memory and CPU time for maintaining such summaries are usually limited, the quality of the mining results is affected in different ways. Based on selected mining(More)
Due to the inherent characteristics of data streams, appropriate mining techniques heavily rely on window-based processing and/or (approximating) data summaries. Because resources such as memory and CPU time for maintaining such summaries are usually limited, the quality of the mining results is affected in different ways. Based on Frequent Itemset Mining(More)
Detecting bursts in data streams is an important and challenging task. Due to the complexity of this task, usually burst detection cannot be formulated using standard query operators. Therefore, we show how to integrate burst detection for stationary as well as non-stationary data into query formulation and processing, from the language level to the(More)
In the past couple of years, sensor networks have evolved into an important infrastructure component for monitoring and tracking events and phenomena in several, often mission critical application domains. An important task in processing streams of data generated by these networks is the detection of anomalies, e.g., outliers or bursts, and in particular(More)
Data streams have become ubiquitous in recent years and are handled on a variety of platforms, ranging from dedicated high-end servers to battery-powered mobile sensors. Data stream processing is therefore required to work under virtually any dynamic resource constraints. Few approaches exist for stream mining algorithms that are capable to adapt to given(More)
Advancements in GPS-technology have spurred major research and development activities for managing and analyzing large amounts of position data of mobile objects. Data mining tasks such as the discovery of movement patterns, classification and outlier detection in the context of object trajectories, and the prediction of future movement patterns have become(More)