TY - JOUR A1 - Haupt, Johannes A1 - Bender, Benedict A1 - Fabian, Benjamin A1 - Lessmann, Stefan T1 - Robust identification of email tracking BT - a machine learning approach JF - European Journal of Operational Research N2 - 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. KW - Analytics KW - Data privacy KW - Email tracking KW - Machine learning Y1 - 2018 U6 - https://doi.org/10.1016/j.ejor.2018.05.018 SN - 0377-2217 SN - 1872-6860 VL - 271 IS - 1 SP - 341 EP - 356 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Bender, Benedict A1 - Grum, Marcus T1 - Gamification and dynamisation of the continous improvement processes BT - design and realization of a gamification platform for continous improvement T2 - International Conference on Electrical, Computer and Energy Technologies N2 - The idea of the continuous improvement process (CIP) helps companies to continuously improve their operation and thereby contributes to their competitiveness. Through digi tization, new potentials emerge to solve known CIP issues. This contribution specifically addresses the individual motivation of employees to contribute to the CIP. Typically, related initiatives lack contributions over time. The use of gamification is a promising way to achieve continuous participation by addressing the individual needs of participants. While the use of extrinsic motivation elements is common in practice, the idea of this approach is to specifically address intrinsic motivations which serve as a long-term motivator. This article contributes to a gam-ification concept for the continuous improvement process. The main results include an adapted CIP, a gamification concept, and a market mechanism. Furthermore, the concept is implemented and demonstrated as a prototype in an online platform. Y1 - 2021 U6 - https://doi.org/10.1109/ICECET52533.2021.9698530 SP - 1 EP - 7 PB - IEEE CY - New York ER - TY - CHAP A1 - Grum, Marcus A1 - Bender, Benedict A1 - Alfa, Attahiru S. T1 - The construction of a common objective function for analytical infrastructures T2 - 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC) N2 - 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. KW - Analytic Infrastructures KW - Cyber-Physical Manufacturing Systems KW - Measuring Efficient Task Processing Y1 - 2018 U6 - https://doi.org/10.1109/ICE.2017.8279892 SP - 219 EP - 225 PB - IEEE CY - New York ER - TY - CHAP A1 - Bender, Benedict A1 - Grum, Marcus T1 - Entwicklung eines Architekturkonzepts zum flexiblen Einsatz von Analytics T2 - Proceedings INFORMATIK - Jahrestagung der Gesellschaft für Informatik e.V. ; Lecture Notes in Informatics (LNI) N2 - 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. KW - Analytics KW - Architekturkonzept KW - Cyber-Phsysische Systeme KW - Cloud KW - Internet of Things Y1 - 2016 UR - https://dl.gi.de/handle/20.500.12116/1189 IS - P259 SP - 815 EP - 824 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Eigelshoven, Felix A1 - Ullrich, André A1 - Bender, Benedict T1 - Public blockchain BT - a systematic literature review on the sustainability of consensus algorithms T2 - Proceedings of the 28th European Conference on Information Systems (ECIS)- A Virtual AIS Conference N2 - Blockchain has the potential to change business transactions to a major extent. Thereby, underlying consensus algorithms are the core mechanism to achieve consistency in distributed infrastructures. Their application aims for transparency and accountability in societal transactions. As a result of missing reviews holistically covering consensus algorithms, we aim to (1) identify prevalent consensus algorithms for public blockchains, and (2) address the resource perspective with a sustainability consideration (whereby we address the three spheres of sustainability). Our systematic literature review identified 33 different consensus algorithms for public blockchains. Our contribution is twofold: first, we provide a systematic summary of consensus algorithms for public blockchains derived from the scientific literature as well as real-world applications and systemize them according to their research focus; second, we assess the sustainability of consensus algorithms using a representative sample and thereby highlight the gaps in literature to address the holistic sustainability of consensus algorithms. KW - Blockchain KW - Consensus algorithms KW - Sustainability KW - Systematic literature revieew Y1 - 2020 UR - https://aisel.aisnet.org/ecis2020_rp/202 SP - 1 EP - 19 ER - TY - CHAP A1 - Bender, Benedict A1 - Szadowiak, Andrzej Marcin T1 - Feature removal on software platforms BT - discontinued core features on browser platformsa case study on mozilla firefox T2 - IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) N2 - Software platforms allow for the extension of features by third-party contributors. Thereby, platform innovation is an important aspects of platforms attractiveness for users and complementors. While previous research focused the introduction of new features, the aspect of feature removal and discontinued features on software platforms has been disregarded. To explore the phenomenon and motivations for feature removal on software platforms, a review of recent literature is provided. To illustrate the existence of and motivations for feature removal, a case study of the browser platform Mozilla Firefox is presented. The results reveal feature removal to regularly occur on browser platforms for user- and developer-related features. Frequent reasons for feature removal involve unused features, security concerns, and bugs. Related motivations for feature removal are discussed from the platform owner's perspective. Implications for complementors and users are highlighted. KW - Software Platforms KW - Discontinued Features KW - Feature Removal KW - Lean Core KW - Platform Innovation KW - Browser Platform KW - Mozilla Firefox Y1 - 2021 U6 - https://doi.org/10.1109/ICE/ITMC52061.2021.9570245 SP - 1 EP - 9 PB - IEEE CY - New York ER - TY - JOUR A1 - Bender, Benedict A1 - Lass, Sander A1 - Habib, Natalie A1 - Scheel, Laura T1 - Plattform-Bereitstellungsstrategien im Maschinen- und Anlagenbau T1 - Platform Delivery Strategies in Mechanical and Plant Engineering BT - Strategien deutscher Unternehmen im Industrie 4.0-Kontext BT - Strategies of German Companies in the Industry 4.0 Context JF - HMD - Praxis der Wirtschaftsinformatik N2 - Digitale Plattformen finden zunehmende Verbreitung in unterschiedlichen Industriezweigen. Immer mehr Unternehmen sind an der Erschließung verbundener Potenziale für ihr Geschäft interessiert. Im Maschinen- und Anlagenbau wird die Vernetzung von Maschinen zunehmend ein Wettbewerbsfaktor für Hersteller. Der Einsatz digitaler Plattformen im Maschinen- und Anlagenbau bietet Herstellern Möglichkeiten zur gezielten Erweiterung des Geschäftsmodells. Für die Bereitstellung digitaler Plattformen können Unternehmen auf unterschiedliche Strategien zurückgreifen. Hierbei sollten Unternehmen die für ihre Konstellation geeignete Variante systematisch identifizieren, um die angestrebten Ziele zu erreichen. Die geeignete Strategie ist von einer Vielzahl an Faktoren abhängig. Als Grundlage für die Identifikation der geeigneten Strategie bietet dieser Beitrag eine systematische Untersuchung der möglichen Bereitstellungsstrategien für Unternehmen. Neben der theoretischen Systematisierung werden gegenwärtig genutzte Strategien am Beispiel des Maschinen- und Anlagenbaus in Deutschland vorgestellt. Zudem werden spezifische Merkmale, welche die Nutzung einer Strategie beeinflussen, als Ansatzpunkt für einen Strategieformulierungsprozess identifiziert. Im Maschinen- und Anlagenbau ist die Bereitstellung einer eigenen Plattform, insbesondere bei Großunternehmen vorherrschend. Die Strategien von KMU unterschieden sich von Großunternehmen. N2 - Digital platforms are becoming increasingly widespread across different industries. More and more companies are interested in developing related potential for their business. In mechanical and plant engineering, the networking of machines becomes increasingly important and a strategic advantage for manufacturers. The use of digital platforms in mechanical and plant engineering offers manufacturers opportunities for targeted expansion of their business model. For the provision of digital platforms, companies can use different strategical approaches. Companies should systematically identify the variant suitable for their constellation in order to achieve the desired objectives. The appropriate strategy depends on a variety of factors. As a basis for the identification of the appropriate strategy, this article offers a systematic overview of the possible deployment strategies for companies. In addition to the theoretical systematization, currently used strategies are presented using the example of the mechanical and plant engineering industry in Germany. In addition, specific features that influence the use of a strategy are identified as a starting point for a strategy formulation process. In mechanical and plant engineering, the provision of an own platform is predominant, especially in large companies. The strategies of SMEs differ from those of large companies. KW - Digitale Plattformen KW - KMU KW - Maschinen- und Anlagenbau KW - Industrie 4.0 KW - Plattform-Bereitstellungsstrategien KW - Digital platforms KW - SME KW - Machinery and plant engineering KW - Industry 4.0 KW - Platform delivery strategies Y1 - 2020 U6 - https://doi.org/10.1365/s40702-020-00648-1 SN - 2198-2775 SN - 1436-3011 IS - 58 SP - 645 EP - 660 PB - Springer CY - Wiesbaden ER - TY - JOUR A1 - Bender, Benedict T1 - The impact of integration on application success and customer satisfaction in mobile device platforms JF - Business & information systems engineering : the international journal of Wirtschaftsinformatik N2 - Digital software platforms allow third parties to develop applications and thus extend their functionality. Platform owners provide platform boundary resources that allow for application development. For developers, platform integration, understood as the employment of platform resources, helps to realize application functionality effectively. Simultaneously, it requires integration effort and increases dependencies. Developers are interested to know whether integration contributes to success in hypercompetitive platform settings. While aspects of platform participation have been studied, research on a comprehensive notion of integration and related implications are missing. By proposing a platform integration model, this study supports a better understanding of integration. Concerning dynamics related to integration, effects were tested using information from over 82,000 Apple AppStore applications. Regression model analysis reveals that application success and customer satisfaction is positively influenced by platform integration. To achieve superior results, developers should address multiple aspects of integration, such as devices, data, the operating system, the marketplace as well as other applications, and provide updates. Finally, the study highlights the importance for all platform participants and their possibilities to employ integration as a strategic instrument. KW - Integration KW - Digital platforms KW - Boundary resources KW - Application success KW - Customer satisfaction KW - Mobile device platforms Y1 - 2020 U6 - https://doi.org/10.1007/s12599-020-00629-0 SN - 2363-7005 SN - 1867-0202 VL - 62 IS - 6 SP - 515 EP - 533 PB - Springer Fachmedien Wiesbaden GmbH CY - Wiesbaden ER - TY - CHAP A1 - Gronau, Norbert A1 - Grum, Marcus A1 - Bender, Benedict T1 - Determining the optimal level of autonomy in cyber-physical production systems T2 - IEEE 14th International Conference on Industrial Informatics (INDIN) N2 - 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. KW - cyber-physical systems KW - hybrid simulation KW - Internet of Things KW - manufacturing systems KW - production engineering computing KW - cyber-physical production systems Y1 - 2017 U6 - https://doi.org/10.1109/INDIN.2016.7819367 SP - 1293 EP - 1299 PB - IEEE CY - New York ER - TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict T1 - Deep reinforcement learning in production systems BT - a systematic literature review JF - International Journal of Production Research N2 - Shortening product development cycles and fully customizable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimization of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyze safety aspects and demonstrate reliability under prevailing conditions. KW - Machine learning KW - reinforcement learning KW - production control KW - production planning KW - manufacturing processes KW - systematic literature review Y1 - 2021 U6 - https://doi.org/10.1080/00207543.2021.1973138 SN - 1366-588X SN - 0020-7543 VL - 13 IS - 60 PB - Taylor & Francis CY - London ER -