Many automated systems need the capability of automatic change detection without the given detection threshold. This paper presents an automated change detection algorithm in streaming multivariate data. Two overlapping windows are used to quantify the changes. While a window is used as the reference window from which the clustering is created, the other called the current window captures the newly incoming data points. A newly incoming data point can be considered a change point if it is not a member of any cluster. As our clustering-based change detector does not require detection threshold, it is an automated detector. Based on this change detector, we propose a reactive clustering algorithm for streaming data. Our empirical results show that, our clustering-based change detector works well with multivariate streaming data. The detection accuracy depends on the number of clusters in the reference window, the window width.
The clustering-based change detection method proposed here is related to the work on automatic change detection and change detection in multivariate streaming data, and clustering-based change detection, and the reactive work on building and maintaining of model. As automated systems require the capability of real-time processing, and adapting to the changing environments, automated change detection plays an important role in many automated systems. One of the models of real-time processing is data stream processing. For example, sensor networks need the automated change detection methods in which detection threshold must adapt to the changes of the environment. Automated change detection method is also important in many mobile robotic applications @cite_1 . For example, @cite_1 have proposed an online change detection method for mobile robots based on the segmentation approach. Recently @cite_11 have developed Data3, a Kinect interface for human motion detection. In fact, Data3 is a kind of system capable of detecting the changes in spatial-temporal streaming data.
@cite_1 @cite_11
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The high cost of damaging an expensive robot or injuring people or equipment in its environment make even rare failures unacceptable in many mobile robot applications. Often the objects that pose the highest risk for a mobile robot are those that were not present throughout previous successful traversals of an environment. Change detection, a closely related problem to novelty detection, is therefore of high importance to many mobile robotic applications that require a robot to operate repeatedly in the same environment. We present a novel algorithm for performing online change detection based on a previously developed robust online novelty detection system that uses a learned lower-dimensional representation of the feature space to perform measures of similarity.We then further improve this change detection system by incorporating online scene segmentation to better utilize contextual information in the environment. We validate these approaches through extensive experiments onboard a large outdoor mobile robot. Our results show that our approaches are robust to noisy sensor data and moderate registration errors and maintain their performance across diverse natural environments and conditions. 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 data^3 provides a basic natural interaction OLAP interface for a sample star schema database using Microsoft's Kinect.