Change Detection in Streaming Data
Dang-Hoan Tran · Common Library Network (Der Gemeinsame Bibliotheksverbund) · 2013
Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times or different locations in space. In the streaming context, it is the process of segmenting a data stream into different segments by identifying the points where the stream dynamics changes. Decentralized change detection can be used in many interesting, and important applications such environmental observing systems, medicare monitoring systems. Although there is great deal of work on distributed detection and data fusion, most of work focuses on the one-time change detection solutions. One-time change detection method requires to proceed data once in response to the change occurring. The trade-off of a continuous distributed detection of changes include detection accuracy, spaceefficiency, detection delay, and communication-efficiency. To achieve these goals, the wildfire warning system is used as a motivating scenario. From the challenges and requirements of the wildfire warning system, the change detection algorithms for streaming data are proposed a part of the solution to the wildfire warning system. By selecting various models of local change detection, different schemes for distributed change detections, and the data exchange protocols, different designs can be achieved. Based on this approach, the contributions of this dissertation are as follows. A general two-window framework for detecting changes in a single data stream is presented. A general synopsis-based change detection framework is proposed. Theoretical and empirical analysis shows that the detection performance of synopsisbased detector is similar to that of non-synopsis change detector if a distance function quantifying the changes is preserved under the process of constructing synopsis. A clustering-based change detection and clustering maintenance method over sliding window is presented. Clustering-based detector can automatically detect the changes in the multivariate streaming data. A framework for decentralized change detection in wireless sensor networks is proposed. A distributed framework for clustering streaming data is proposed by extending the two-phased stream clustering approach which is widely used to cluster a single data stream.