Use Cases in Dataflow-Based Privacy and Trust Modeling and Analysis in Industry 4.0 Systems

Fostering efficiency of distributed supply chains in the Industry 4.0 often bases on IoT-data analysis and by means of lean- and shop oor-management. However, trust by preserving privacy is a precondition: Competing factories will not share data, if, e.g., the analysis of the data will reveal business relevant information to competitors. Our approach is enforcing privacy policies in Industry 4.0 supply chains. These are highly dynamic and therefore not manageable by \u27traditional\u27 rights-management approaches as we will stretch in a literature analysis. To enforce privacy, we analyze two industrial settings and derive general requirements: (1) Lean- and shop oor-management and (2) factory access control, both common in Industry 4.0 supply chains. We further propose a reference architecture for Industry 4.0 supply chains. We introduce the combination of Palladio Component Model (PCM) [23] and Ensembles [4] in order to analyze and enforce privacy policies in highly dynamic environments. Our novel approach paves way for data sharing and global data analyzes in highly dynamic Industry 4.0 supply chains. It is an important step for efficiency of these supply chains and therefore one important cornerstone for the success of Industry 4.0

An Architecture-Based Approach for Compute-Intensive Pervasive Systems in Dynamic Environments

International audienceDistributed systems have continued to evolve and we note two important trends: the dramatically increasing level of dynamism in contemporary distributed systems and the convergence of mobile computing with cloud computing. The end result is that it is very difficult to achieve the required level of scalability and dependability in a systematic way when considering pervasive systems that are software-and compute-intensive and whose functionality is typically augmented by static cloud infrastructure resources. This work discusses relevant challenges and requirements for integrating cloud computing with pervasive systems operating in dynamic environments. We present a set of requirements using a holistic case study and describe a reference architecture to address these requirements

Users' (dis)satisfaction with the personalTV application: combining objective and subjective data

The overabundance of content on online video platforms has made intelligent recommender systems that assist users in finding content matching their personal preferences indispensable. This article reports on a study in which "PersonalTV," an online video recommendation application that has been developed for research purposes, was evaluated by a panel of test users for the first time. In view of this, objective implicit and subjective explicit user feedback were triangulated. The "PersonalTV" application enables its users to explore and watch videos from the YouTube library. It builds up a personal viewing profile in order to give personalized content suggestions. We investigated the relation between the recommended content and the consumption percentage (RQ 1), between the recommended content and the reported satisfaction (RQ 2), and explored whether these objective and subjective measures converge (RQ 3). Additional user feedback that may help to improve the application was collected. © 2011 ACM.status: publishe