@phdthesis{Noack2014, author = {Noack, Tino}, title = {A knowledge discovery cycle for monitoring mobile cyber-physical systems}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-33676}, school = {BTU Cottbus - Senftenberg}, year = {2014}, abstract = {Mobile cyber-physical systems (MCPSs) such as motor vehicles, railed vehicles, aircraft, or spacecraft are commonly used in our life today. These systems are location-independent and embedded in a physical environment which is usually harsh and uncertain. MCPSs are equipped with a wide range of sensors that continuously produce sensor data streams. It is mandatory to process these data streams in an appropriate manner in order to satisfy different monitoring objectives, and it is anticipated that the complexity of MCPSs will continue to increase in the future. For instance, this includes the system description and the amount of data that must be processed. Accordingly, it is necessary to monitor these systems in order to provide reliability and to avoid critical damage. Monitoring is usually a semi-automatic process while human experts are responsible for consequent decisions. Thus, appropriate monitoring approaches are required to both provide a reasonably precise monitoring process and to reduce the complexity of the monitoring process itself. The contribution of the present thesis is threefold. First, a knowledge discovery cycle (KDC) has been developed, which aims to combine the research areas of knowledge discovery in databases and knowledge discovery from data streams to monitor MCPSs. The KDC is a cyclic process chain comprising an online subcycle and an offline subcycle. Second, a new data stream anomaly detection algorithm has been developed. Since it is necessary to identify a large number of system states automatically during operation, data stream anomaly detection becomes a key task for monitoring MCPSs. Third, the KDC and the anomaly detection algorithm have been prototypically implemented and a case study has been performed in a real world scenario relating to the ISS Colombus module.}, subject = {Knowledge Discovery Cycle; Mobile Cyber-Physical System; Monitoring; Data Mining; Anomaly Detection; Anomalieerkennung; Data Mining; {\"U}berwachung; Mobiles cyber-physikalisches System; Knowledge Discovery Cycle; Columbus ; Data Mining; Zustands{\"u}berwachung}, language = {en} } @techreport{NoackSchmittSaretz2013, author = {Noack, Tino and Schmitt, Ingo and Saretz, Sascha}, title = {OVA-based multi-class classification for data stream anomaly detection}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-28187}, year = {2013}, abstract = {Mobile cyber-physical systems (MCPSs), such as the International Space Station, are equipped with sensors which produce sensor data streams. Continuous changes like wear and tear influence the system states of a MCPS continually during runtime. Hence, monitoring is necessary to provide reliability and to avoid critical damage. Although, the monitoring process is limited by resource restrictions. Therefore, the focal point of the present paper is on time-efficient multi-class data stream anomaly detection. Our contribution is bifid. First, we use a one-versusall classification model to combine a set of heterogeneous one-class classifiers consecutively. Such a chain of one-class classifiers provides a very flexible structure while the administrative overhead is reasonably low. Second, based on the classifier chain, we introduce classifier pre-selection.}, subject = {Informatik}, language = {en} }