Motion sensing input devices like Microsoft's Kinect offer an alternative to traditional computer input devices like keyboards and mouses. Daily new applications using this interface appear. Most of them implement their own gesture detection. In our demonstration we show a new approach using the data stream engine Andu IN. The gesture detection is done based on Andu IN's complex event processing functionality. This way we build a system that allows to define new and complex gestures on the basis of a declarative programming interface. On this basis our demonstration data3provides a basic natural interaction OLAP interface for a sample star schema database using Microsoft's Kinect.

As a special type of wireless P2P systems, sensor networks are often deployed for detecting events caused by disasters. The peer-to-peer mode of the sensor system itself gets challenged either directly by damages of the disaster or by unreliable wireless links. This work explores possible failure models and compares the performance of several gossip protocols corresponding to the failures models. With further refinement of the gossip protocols, the performance in the failure modes caused by disasters is improved. The evaluation of our simulation results shows that using refined gossip protocols in correspondence to the failure models, the information aggregation and dissemination speed, communication cost and the accuracy of the aggregated data can be improved.

Sensor networks have evolved to a powerful infrastructure component for event monitoring in many application scenarios. In addition to simple filter and aggregation operations, an important task in processing sensor data is data mining - the identification of relevant information and patterns. Limited capabilities of sensor nodes in terms of storage and processing capacity, battery lifetime, and communication demand a power-efficient, preferably sensor-local processing. In this paper, we present AnduIN, a system for developing, deploying, and running in-network data mining tasks. The system consists of a data stream processing engine, a library of operators for sensor-local processing, a box-and-arrow editor for specifying data mining tasks and deployment, a GUI providing the user with current information about the network and running queries, and an alerter notifying the user if a better query execution plan is available. At the demonstration site, we plan to show our system in action using burst detection as example application.

Materializing results from complex aggregation queries helps to significantly improve response times in OLAP servers. This problem is known as the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper we present an online approach for adjusting the configuration dynamically to the current workload. This approach is implemented as part of an open source OLAP server and acts on the level of multidimensional MDX queries. The work presents the details of cost estimation and optimization of the system demonstrated in [10] and extends it by an online tuning strategy.

OLAP servers based on relational backends typically exploit materialized aggregate tables to improve response times of complex analytical queries. One of the key problems in this context is the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper, we present a system that continuously monitors the workload and raises a quantified alert, when a better configuration is available. We address the tasks of query monitoring and view selection at the OLAP level instead of the SQL level, which simplifies the containment checks as well as rewriting and in this way helps to reduce the complexity of the backend system. At the demo we plan to show how our system works, i.e., how the system reacts upon arbitrary (interactive) workloads and how the user is alerted that a better configuration is available.

Evolving from heterogeneous database systems one of the main problems in peer data management systems (PDMS) is distributed query processing. With the absence of global knowledge such strategies have to focus on routing the query efficiently to only those peers that are most likely to contribute to the final result. Using routing indexes is one possibility to achieve this. Since data may change over time these structures have to be updated and maintained which can be very expensive. In this paper, we present a novel kind of routing indexes that enables efficient query routing. Furthermore, we propose a threshold based update strategy that can help to reduce maintenance costs by far. We exemplify the benefit of these indexes using a distributed skyline strategy as an example. Finally, we show how relaxing exactness requirements, that are usually posed on results, can compensate the use of slightly outdated index information