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The microservice architectural style has gained much attention from both academia and industry recently as a novel way to design, develop, and deploy cloud-native applications. This concept encourages the decomposition of a monolith into multiple independently deployable units. A typical microservices-based application is formed of two service types: functional services, which provide the core business logic, and infrastructure services, which provide essential functionalities for a microservices ecosystem. To improve developers' productivity, many software frameworks have been developed to provide those reusable infrastructure services, allowing programmers to focus on implementing microservices in arbitrary ways. In this work, we made use of four open source frameworks to develop a cloud-based application in order to compare and evaluate their usability and practicability. While all selected frameworks promote asynchronous microservice design in general, there are differences in the ways each implements services. This leads to interoperability issues, such as message topic naming convention. Additionally, a key finding is the long startup times of JVM-based services that might reduce application's resiliency and portability. Some other advantages come directly from the programming language, such as the ability of Go to generate native binary executables, which results in very small and compact Docker images (up to 78\% smaller compared to other languages).

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app track your daily routine (TYDR), which tracks and records smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than many other existing apps, including metadata on notifications, photos taken, and music played back by the user. Based on the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we developed the Privacy Model for Mobile Data Collection Applications (PM-MoDaC) specifically tailored for apps that are related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Our experimental evaluation is based on data collected with TYDR during a two-month period. We find evidence that our users accept our proposed privacy model. Based on data about granting TYDR all or no Android system permissions, we find evidence that younger users tend to be less willing to share their data (average age of 30 years compared to 35 years). We also observe that female users tend to be less willing to share data compared to male users. We did not find any evidence that education or personality traits are a factor related to data sharing. TYDR users score higher on the personality trait openness to experience than the average of the population, which we assume to be evidence that the type of app influences the user base it attracts in terms of average personality traits.

Through lockdowns and other severe changes to daily life, almost everyone is affected by the COVID-19 pandemic. Scientists and medical doctors are - among others - mainly interested in researching, monitoring, and improving physical and mental health of the general population. Mobile health apps (mHealth), and apps conducting ecological momentary assessments (EMA) respectively, can help in this context. However, developing such mobile applications poses many challenges like costly software development efforts, strict privacy rules, compliance with ethical guidelines, local laws, and regulations. In this paper, we present TrackYourHealth (TYH), a highly configurable, generic, and modular mobile data collection and EMA platform, which enabled us to develop and release two mobile multi-platform applications related to COVID-19 in just a few weeks. We present TYH and highlight specific challenges researchers and developers of similar apps may also face, especially when developing apps related to the medical field., Accepted for publication in the proceedings of the 2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft)

Several governments introduced or promoted the use of contact-tracing apps during the ongoing COVID-19 pandemic. In Germany, the related app is called Corona-Warn-App, and by end of 2020, it had 22.8 million downloads. Contact tracing is a promising approach for containing the spread of the novel coronavirus. It is only effective if there is a large user base, which brings new challenges like app users unfamiliar with using smartphones or apps. As Corona-Warn-App is voluntary to use, reaching many users and gaining a positive public perception is crucial for its effectiveness. Based on app reviews and tweets, we are analyzing the public perception of Corona-Warn-App. We collected and analyzed all 78,963 app reviews for the Android and iOS versions from release (June 2020) to beginning of February 2021, as well as all original tweets until February 2021 containing #CoronaWarnApp (43,082). For the reviews, the most common words and n-grams point towards technical issues, but it remains unclear, to what extent this is due to the app itself, the used Exposure Notification Framework, system settings on the user's phone, or the user's misinterpretations of app content. For Twitter data, overall, based on tweet content, frequent hashtags, and interactions with tweets, we conclude that the German Twitter-sphere widely reports adopting the app and promotes its use., Accepted for publication at the 34th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2021)

This chapter introduces TYDR—Track Your Daily Routine, an Android app that enables the research of the relationship between smartphone sensor data and usage statistics and the users’ personality via mobile crowdsensing. TYDR processes and visualizes the tracked data as well as the results of the personality questionnaires. We released TYDR on Google Play in October 2018 and registered 3,921 installations since then. To the best of our knowledge, we are the first to propose a full-scale, integrated privacy model specifically for apps related to mobile collection of data. Our privacy model called PM-MoDaC (Privacy Model for Mobile Data Collection Apps) consists of nine concrete measures to be taken to ensure the users’ privacy. These measures include transparently informing the users about the data being collected, the anonymization of user data, and enabling an opt-out option. We present the implementation of all the privacy measures in TYDR. We collected data with TYDR over the course of a two-months period and extensively evaluated our privacy model and which data users are willing to share. Based on data about granting TYDR all or no Android system permissions, we found evidence that younger users tend to be less willing to share their data (average age of 30 years compared to 35 years). We also observed that female users tend to be less willing to share data compared to male users. We did not find any evidence that education or personality traits are a factor related to data sharing. TYDR users score higher on the personality trait openness to experience than the average of the population, which we assume to be evidence that the type of app influences the user base it attracts in terms of average personality traits.

In recent decades, it has become a significant tendency for industrial manufacturers to adopt decentralization as a new manufacturing paradigm. This enables more efficient operations and facilitates the shift from mass to customized production. At the same time, advances in data analytics give more insights into the production lines, thus improving its overall productivity. The primary objective of this paper is to apply a decentralized architecture to address new challenges in industrial analytics. The main contributions of this work are therefore two-fold: (1) an assessment of the microservices' feasibility in industrial environments, and (2) a microservices-based architecture for industrial data analytics. Also, a prototype has been developed, analyzed, and evaluated, to provide further practical insights. Initial evaluation results of this prototype underpin the adoption of microservices in industrial analytics with less than 20ms end-to-end processing latency for predicting movement paths for 100 autonomous robots on a commodity hardware server. However, it also identifies several drawbacks of the approach, which is, among others, the complexity in structure, leading to higher resource consumption., Accepted to be published at the IEEE International Conference on Industrial Cyber-Physical Systems 2019

In this experience paper, we summarize the analysis of key technologies that are prerequisite for a secure realtime communication and computing Infrastructure in the smart factory, which supports flexible and reconfigurable production assets with a real-time digital representation. A broad range of Industry 4.0 use cases is evaluated – jointly with industrial application partners - and a set of basic requirements is derived from them. These challenges need to be addressed by available and upcoming ICT technologies in the domains of cloud computing in an industrial environment, virtualization and industrial Edge Computing, 5G radio and network, analytics with big and fast data, and Artificial Intelligence / Machine Learning technologies. The analysis includes mechanisms for secure and reliable connectivity in production, secure wireless communication and secure processes, massive sensor data analysis, and (virtual) network elements like secure gateway. Challenges and opportunities for new applications in production are described in the following.

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user's personality. As filling out personality questionnaires is tedious, we propose the prediction of the user's personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user's different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user's personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications., Comment: Accepted for publication at the 15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018)

Recommender systems are used to filter through vast amounts of items and recommend those that potentially have the highest relevance for the user. Recently, research dealing with recommendations in academia increased. In this paper, we analyze to what extent social relations from existing data can be utilized to generate academic conference recommendations. We design and implement a social recommender system and show how, without the need for explicit ratings, viable recommendations can be made, while at the same time reducing the cost of kNN-neighborhood selection.

As of today, communication habits are shifting towards Online Social Network (OSN) services such as WhatsApp or Facebook. Still, OSN platforms are mostly built in a closed, proprietary manner that disallows users from communicating seamlessly between different OSN services. These lock-in effects are used to discourage users to migrate to other services. To overcome the obvious drawbacks of proprietary protocols and service architectures, SONIC proposes a holistic approach that facilitates seamless connectivity between different OSN platforms and allows user accounts to be migrated between OSN platforms without losing data or connections to other user profiles. Thus, SONIC builds the foundation for an open and heterogeneous Online Social Network Federation (OSNF). In this paper, we present a distributed and domain-independent ID management architecture for the SONIC OSNF, which allows user identifiers (GlobalID) to remain unchanged even when a profile is migrated to a different OSN platform. In order to resolve a given GlobalID to the actual URL of a social profile the Global Social Lookup System (GSLS), a distributed directory service built on peer to peer technology is introduced. Datasets called Social Records, which comprise all information required to look up a certain profile, are stored and published by the GSLS. Following this approach, social profiles can be migrated between OSN platforms without changing the user identifier, or losing connections to other users’ social profiles.

Online Social Network (OSN) platforms have become an important part of our everyday online lives. We communicate, share content, and organize meetings and events using social platforms and services. However, even though there is a strong trend towards OSN services to become the main communication medium, most OSN platforms are still proprietary, closed services that keep users from connecting directly and seamlessly to the services of other OSN platforms. The resulting lock-in effects are intentionally created by OSN operators, as their business models are built mostly on targeted advertisement services. While open and decentralized communication protocols exist for most other aspects of digital social interaction, there are only few micro-standards and protocols in existence for the social web. A holistic standard is yet missing. We envision a truly open and decentralized ecosystem of OSN platforms, where users are not cut off from friends using other social platforms and can freely migrate from one OSN platform to another at any time without losing established relationships in the social graph. This would allow users to freely choose an OSN platform of their liking instead of being limited in their choice to the platform used by one's friends. In this paper, we give an overview about the SOcial Network InterConnect protocol (SONIC), a novel social protocol for seamless social cross-platform communication. SONIC proposes a decentralized and heterogeneous Online Social Network Federation (OSNF), in which platforms are connected via a communication protocol. This allows different OSN platforms to seamlessly communicate with each other, while giving users the ability to migrate social profiles between platforms on demand without losing previously established connections to other user profiles.

With the spread of fast mobile Internet connections, such as 3G and LTE and the increasing processor power of mobile devices accessing cloud computing services on-the-go is common among all users. Sharing private information with friends and family members are options of popular cloud services, such as storage and social media services. But recent headlines show that the access to private information is often not sufficiently secured on the service level. The approach presented in this paper aims to use attribute-based meta-information to secure data on the level of files without relying on additional functionality of third-party services. A mobile device app is used to access and alter the meta-information. Attribute-based encryption mechanisms secure the private data and define access policies for friends and other users simultaneously.

Online Social Networks have become one of the main tools for interpersonal online communication. In the age of the smartphone, mobile user scenarios become more and more important for Online Social Networks. Smartphones enable location-based and context-aware services, but bring the increased risk of privacy violations - at least in centralized OSN architectures. Decentralized Online Social Network architectures are promising as they inherently offer better privacy and less dependence on a single service provider, but they bring new challenges regarding core features of Online Social Networks. In this paper, we introduce a three-tiered view of the social graph and propose a new architecture for decentralized Online Social Networking applications, supporting the three-tiered view and focusing on location-based and context-aware user scenarios.