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E-Mail tracking uses personalized links and pictures for gathering information on user behavior, for example, where, when, on what kind of device, and how often an e-mail has been read. This information can be very useful for marketing purposes. On the other hand, privacy and security requirements of customers could be violated by tracking. This paper examines how e-mail tracking works, how it can be detected automatically, and to what extent it is used in German e-commerce. We develop a detection model and software tool in order to collect and analyze more than 600 newsletter e-mails from companies of several different industries. The results show that the usage of e-mail tracking in Germany is prevalent but also varies depending on the industry.
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.
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.
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%.
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.
The paper deals with the increasing growth of embedded systems and their role within structures similar to the Internet (Internet of Things) as those that provide calculating power and are more or less appropriate for analytical tasks. Faced with the example of a cyber-physical manufacturing system, a common objective function is developed with the intention to measure efficient task processing within analytical infrastructures. A first validation is realized on base of an expert panel.
Coring on Digital Platforms
(2017)
Today’s mobile devices are part of powerful business ecosystems, which usually involve digital platforms. To better understand the complex phenomenon of coring and related dynamics, this paper presents a case study comparing iMessage as part of Apple’s iOS and WhatsApp. Specifically, it investigates activities regarding platform coring, as the integration of several functionalities provided by third-party applications in the platform core. The paper makes three contributions. First, a systematization of coring activities is developed. Coring modes are differentiated by the amount of coring and application maintenance. Second, the case study revealed that the phenomenon of platform coring is present on digital platforms for mobile devices. Third, the fundamentals of coring are discussed as a first step towards theoretical development. Even though coring constitutes a potential threat for third-party developers regarding their functional differentiation, an idea of what a beneficial partnership incorporating coring activities could look like is developed here.
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking experiment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders. (C) 2018 Elsevier B.V. All rights reserved.