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Die optimale Dimensionierung von IT-Hardware stellt Entscheider aufgrund der stetigen Weiterentwicklung zunehmend vor Herausforderungen. Dies gilt im Speziellen auch für Analytics-Infrastrukturen, die zunehmend auch neue Software zur Analyse von Daten einsetzen, welche in den Ressourcenanforderungen stark variieren. Damit eine flexible und gleichzeitig effiziente Gestaltung von Analytics-Infrastrukturen erreicht werden kann, wird ein dynamisch arbeitendes Architekturkonzept vorgeschlagen, das Aufgaben auf Basis einer systemspezifischen Entscheidungsmaxime mit Hilfe einer Eskalationsmatrix verteilt und hierfür Aufgabencharakteristiken sowie verfügbare Hardwareausstattungen entsprechend ihrer Auslastung berücksichtigt.
E-Mail Tracking
(2016)
E-mail advertisement, as one instrument in the marketing mix, allows companies to collect fine-grained behavioural data about individual users’ e-mail reading habits realised through sophisticated tracking mechanisms. Such tracking can be harmful for user privacy and security. This problem is especially severe since e-mail tracking techniques gather data without user consent. Striving to increase privacy and security in e-mail communication, the paper makes three contributions. First, a large database of newsletter e-mails is developed. This data facilitates investigating the prevalence of e- mail tracking among 300 global enterprises from Germany, the United Kingdom and the United States. Second, countermeasures are developed for automatically identifying and blocking e-mail tracking mechanisms without impeding the user experience. The approach consists of identifying important tracking descriptors and creating a neural network-based detection model. Last, the effectiveness of the proposed approach is established by means of empirical experimentation. The results suggest a classification accuracy of 99.99%.
Traditional production systems are enhanced by cyber-physical systems (CPS) and Internet of Things. A kind of next generation systems, those cyber-physical production systems (CPPS) are able to raise the level of autonomy of its production components. To find the optimal degree of autonomy in a given context, a research approach is formulated using a simulation concept. Based on requirements and assumptions, a cyber-physical market is modeled and qualitative hypotheses are formulated, which will be verified with the help of the CPPS of a hybrid simulation environment.
In times of digitalization, the collection and modeling of business processes is still a challenge for companies. The demand for trustworthy process models that reflect the actual execution steps therefore increases. The respective kinds of processes significantly determine both, business process analysis and the conception of future target processes and they are the starting point for any kind of change initiatives. Existing approaches to model as-is processes, like process mining, are exclusively focused on reconstruction. Therefore, transactional protocols and limited data from a single application system are used. Heterogeneous application landscapes and business processes that are executed across multiple application systems, on the contrary, are one of the main challenges in process mining research. Using RFID technology is hence one approach to close the existing gap between different application systems. This paper focuses on methods for data collection from real world objects via RFID technology and possible combinations with application data (process mining) in order to realize a cross system mining approach.