Dr. rer. nat. Benjamin Erb

Future vehicles and infrastructure will rely on data from external entities such as other vehicles via V2X communication for safety-critical applications. Malicious manipulation of this data can lead to safety incidents. Earlier works proposed a trust assessment framework (TAF) to allow a vehicle or infrastructure node to assess whether it can trust the data it received. Using subjective logic, a TAF can calculate trust opinions for the trustworthiness of the data based on different types of evidence obtained from diverse trust sources. One particular challenge in trust assessment is the appropriate quantification of this evidence. In this paper, we introduce different quantification methods that transform evidence into appropriate subjective logic opinions. We suggest quantification methods for different types of evidence: security reports, misbehavior detection reports, intrusion detection system alerts, GNSS spoofing scores, and system integrity reports. Our evaluations in a smart traffic light system scenario show that the TAF detects attacks with an accuracy greater than 96% and intersection throughput increased by 42% while maintaining safety and security, when using our proposed quantification methods.

Vehicular communication via V2X networks increases road safety, but is vulnerable to data manipulation which can lead to serious incidents. Existing security systems, such as misbehavior detection systems, have limitations in detecting and mitigating such threats. To address these challenges, we have implemented a software prototype of a Trust Assessment Framework (TAF) that assesses the trustworthiness of received V2X data by integrating evidence from multiple trust sources. This interactive demonstration illustrates the quantification of trust for a smart traffic light system application. We demonstrate the impact of varying evidence coming from a misbehavior detection system and a security report generator on the trust assessment process. We also showcase internal processing steps within our TAF when receiving new evidence, up to and including the eventual decision making on the trustworthiness of the received V2X data.

Taking part in surveys, experiments, and studies is often compensated by rewards to increase the number of participants and encourage attendance. While privacy requirements are usually considered for participation, privacy aspects of the reward procedure are mostly ignored. To this end, we introduce PrePaMS, an efficient participation management system that supports prerequisite checks and participation rewards in a privacy-preserving way. Our system organizes participations with potential (dis-)qualifying dependencies and enables secure reward payoffs. By leveraging a set of proven cryptographic primitives and mechanisms such as anonymous credentials and zero-knowledge proofs, participations are protected so that service providers and organizers cannot derive the identity of participants even within the reward process. In this paper, we have designed and implemented a prototype of PrePaMS to show its effectiveness and evaluated its performance under realistic workloads. PrePaMS covers the information whether subjects have participated in surveys, experiments, or studies. When combined with other secure solutions for the actual data collection within these events, PrePaMS can represent a cornerstone for more privacy-preserving empirical research.

The capability to isolate system resources is an essential characteristic of virtualisation technologies and is therefore important for research and industry alike. It allows the co-location of experiments and workloads, the partitioning of system resources and enables multi-tenant business models such as cloud computing. Poor isolation among tenants bears the risk of noisy-neighbour and contention effects which negatively impacts all of those use-cases. These effects describe the negative impact of one tenant onto another by utilising shared resources. Both industry and research provide many different concepts and technologies to realise isolation. Yet, the isolation capabilities of all these different approaches are not well understood; nor is there an established way to measure the quality of their isolation capabilities. Such an understanding, however, is of uttermost importance in practice to elaborately decide on a suited implementation. Hence, in this work, we present a novel methodology to measure the isolation capabilities of virtualisation technologies for system resources, that fulfils all requirements to benchmarking including reliability. It relies on an immutable approach, based on Experiment-as-Code. The complete process holistically includes everything from bare metal resource provisioning to the actual experiment enactment.The results determined by this methodology help in the decision for a virtualisation technology regarding its capability to isolate given resources. Such results are presented here as a closing example in order to validate the proposed methodology.

Background: Stress levels in the general population had already been increasing in recent years, and have subsequently been exacerbated by the global pandemic. One approach for innovative online-based interventions are “chatbots” – computer programs that can simulate a text-based interaction with human users via a conversational interface. Research on the efficacy of chatbot-based interventions in the context of mental health is sparse. The present study is designed to investigate the effects of a three-week chatbot-based intervention with the chatbot ELME, aiming to reduce stress and to improve various health-related parameters in a stressed sample. Methods: In this multicenter, two-armed randomised controlled trial with a parallel design, a three-week chatbot-based intervention group including two daily interactive intervention sessions via smartphone (á 10-20 min.) is compared to a treatment-as-usual control group. A total of 130 adult participants with a medium to high stress levels will be recruited in Germany. Assessments will take place pre-intervention, post-intervention (after three weeks), and follow-up (after six weeks). The primary outcome is perceived stress. Secondary outcomes include self-reported interoceptive accuracy, mindfulness, anxiety, depression, personality, emotion regulation, psychological well-being, stress mindset, intervention credibility and expectancies, affinity for technology, and attitudes towards artificial intelligence. During the intervention, participants undergo ecological momentary assessments. Furthermore, satisfaction with the intervention, the usability of the chatbot, potential negative effects of the intervention, adherence, potential dropout reasons, and open feedback questions regarding the chatbot are assessed post-intervention. Discussion: To the best of our knowledge, this is the first chatbot-based intervention addressing interoception, as well as in the context with the target variables stress and mindfulness. The design of the present study and the usability of the chatbot were successfully tested in a previous feasibility study. To counteract a low adherence of the chatbot-based intervention, a high guidance by the chatbot, short sessions, individual and flexible time points of the intervention units and the ecological momentary assessments, reminder messages, and the opportunity to postpone single units were implemented.

The research field of data analytics has grown significantly with the increase of gathered and available data. Accordingly, a large number of tools, metrics, and best practices have been proposed to make sense of this vast amount of data. To this end, benchmarking and standardization are needed to understand the proposed approaches better and continuously improve them. For this purpose, numerous associations and committees exist. One of them is SPEC (Standard Performance Evaluation Corporation), a non-profit corporation for the standardization and benchmarking of performance and energy evaluations. This paper gives an overview of the recently established SPEC RG Predictive Data Analytics Working Group. The mission of this group is to foster interaction between industry and academia by contributing research to the standardization and benchmarking of various aspects of data analytics.

Digital interactions via the internet have become the norm rather than the exception in our global society. Concerns have been raised about human-centered privacy and the often unreflected self-disclosure behavior of internet users. This study on human-centered privacy follows two major aims: first, investigate the willingness of university students as digital natives to self-disclose private data and information from psychological domains including their person, social and academic life, their mental health as well as their health behavior habits when taking part as a volunteer in a scientific online survey. Second, examine to what extent the participants’ self-disclosure behavior can be modulated by experimental induction of Privacy Awareness (PA) or Trust in Privacy (TIP) or a combination of both (PA and TIP). In addition, the role of human factors such as personality traits, gender or mental health (e.g., self-reported depressive symptoms) on self-disclosure behavior was explored and the influence of PA and TIP induction were considered. Participants were randomly assigned to four experimental groups. In group A (n = 50, 7 males), privacy awareness (PA) was induced implicitly by the inclusion of privacy concern items. In group B (n = 43, 6 males), trust in privacy (TIP) was experimentally induced by buzzwords and by visual TIP primes promising safe data storage. Group C (n = 79, 12 males) received both, PA and TIP induction, while group D (n = 55, 9 males) served as control group. Participants had the choice to answer the survey items by agreeing to one of a number of possible answers including the options to refrain from self-disclosure by choosing the response options “don’t know” or “no answer”. Self-disclosure among participants was high irrespective of experimental group and irrespective of psychological domains of the information provided. The results of this study suggest that willingness of volunteers to self-disclose private data in a scientific online study cannot simply be overruled or changed by any of the chosen experimental privacy manipulations. The present results extend the previous literature on human-centered privacy and despite limitations can give important insights into self-disclosure behavior of young people and the privacy paradox.

The open science movement has taken up the important challenge to increase transparency of statistical analyses, to facilitate reproducibility of studies, and to enhance reusability of data sets. To counter the replication crisis in the psychological and related sciences, the movement also urges researchers to publish their primary data sets alongside their articles. While such data publications represent a desirable improvement in terms of transparency and are also helpful for future research (e.g., subsequent meta-analyses or replication studies), we argue that such a procedure can worsen existing privacy issues that are insufficiently considered so far in this context. Recent advances in de-anonymization and re-identification techniques render privacy protection increasingly difficult, as prevalent anonymization mechanisms for handling participants' data might no longer be adequate. When exploiting publicly shared primary data sets, data from multiple studies can be linked with contextual data and eventually, participants can be de-anonymized. Such attacks can either re-identify specific individuals of interest, or they can be used to de-anonymize entire participant cohorts. The threat of de-anonymization attacks can endanger the perceived confidentiality of responses by participants, and ultimately, lower the overall trust of potential participants into the research process due to privacy concerns.

Modern single page web applications require client-side executions of application logic, including critical functionality such as client-side cryptography. Existing mechanisms such as TLS and Subresource Integrity secure the communication and provide external resource integrity. However, the browser is unaware of modifications to the client-side application as provided by the server and the user remains vulnerable against malicious modifications carried out on the server side. Our solution makes such modifications transparent and empowers the browser to validate the integrity of a web application based on a publicly verifiable log. Our Web Application Integrity Transparency (WAIT) approach requires (1) an extension for browsers for local integrity validations, (2) a custom HTTP header for web servers that host the application, and (3) public log servers that serve the verifiable logs. With WAIT, the browser can disallow the execution of undisclosed application changes. Also, web application providers cannot dispute their authorship for published modifications anymore. Although our approach cannot prevent every conceivable attack on client-side web application integrity, it introduces a novel sense of transparency for users and an increased level of accountability for application providers particularly effective against targeted insider attacks.

Empirical sciences and in particular psychology suffer a methodological crisis due to the non-reproducibility of results, and in rare cases, questionable research practices. Pre-registered studies and the publication of raw data sets have emerged as effective countermeasures. However, this approach represents only a conceptual procedure and may in some cases exacerbate privacy issues associated with data publications. We establish a novel, privacy-enhanced workflow for pre-registered studies. We also introduce PeQES, a corresponding platform that technically enforces the appropriate execution while at the same time protecting the participants' data from unauthorized use or data repurposing. Our PeQES prototype proves the overall feasibility of our privacy-enhanced workflow while introducing only a negligible performance overhead for data acquisition and data analysis of an actual study. Using trusted computing mechanisms, PeQES is the first platform to enable privacy-enhanced studies, to ensure the integrity of study protocols, and to safeguard the confidentiality of participants' data at the same time.

Privacy patterns describe core aspects of privacy-enhancing solutions to recurring problems and can, therefore, be instrumental to the privacy-by-design paradigm. However, the privacy patterns domain is still evolving. While the main focus is currently put on compiling and structuring high-quality privacy patterns in catalogs, the support for developers to select suitable privacy patterns is still limited. Privacy patterns selection-support means, in essence, the quick and easy scoping of a collection of patterns to the most applicable ones based on a set of predefined criteria. To evaluate patterns against these criteria, a thorough understanding of the privacy patterns landscape is required. In this paper, (i) we show that there is currently a lack of extensive support for privacy patterns selection due to the insufficient understanding of pattern properties, (ii) we propose additional properties that need to be analyzed and can serve as a first step towards a robust selection criteria, (iii) we analyze and present the properties for 70 privacy patterns, and (iv) we discuss a potential approach of how such a selection-support method can be realized.

Background: Software agents are computer-programs that conduct conversations with a human. The present study evaluates the feasibility of the software agent “SISU” aiming to uplift psychological wellbeing. Methods: Within a one-group pretest-posttest trial, N = 30 German-speaking participants were recruited. Assessments took place before (t1), during (t2) and after (t3) the intervention. The ability of SISU to guide participants through the intervention, acceptability, and negative effects were investigated. Data analyses are based on intention-to-treat principles. Linear mixed models will be used to investigate short-term changes over time in mood, depression, anxiety. Intervention: The intervention consists of two sessions. Each session comprises writing tasks on autobiographical negative life events and an Acceptance- and Commitment Therapy-based exercise respectively. Participants interact with the software agent on two consecutive days for about 30 min each. Results: All participants completed all sessions within two days. User experience was positive, with all subscales of the user experience questionnaire (UEQ) M > 0.8. Participants experienced their writings as highly self-relevant and personal. However, 57% of the participants reported at least one negative effect attributed to the intervention. Results on linear mixed models indicate an increase in anxiety over time (β = 1.33, p = .001). Qualitative User Feedback revealed that the best thing about SISU was its innovativeness (13%) and anonymity (13%). As worst thing about SISU participants indicated that the conversational style of SISU often felt unnatural (73%). Conclusion: SISU successfully guided participants through the two-day intervention. Moreover, SISU has the potential to enter the inner world of participants. However, intervention contents have the potential to evoke negative effects in individuals. Expectable short-term symptom deterioration due to writing about negative autobiographical life events could not be prevented by acceptance and commitment therapy-based exercises. Hence, results suggest a revision of intervention contents as well as of the conversational style of SISU. The good adherence rate indicates the useful and acceptable format of SISU as a mental health chatbot. Overall, little is known about the effectiveness of software agents in the context of psychological wellbeing. Results of the present trial underline that the innovative technology bears the potential of SISU to act as therapeutic agent but should not be used with its current intervention content. Trial-registration: The Trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Studies Register (DRKS): DRKS00014933 (date of registration: 20.06.2018). Link: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00014933.

Modern applications with increasingly connected domain topologies require processing and programming abstractions that reflect the network structure inherent to these applications. At the same time, data-intensive applications necessitate more and more online processing capabilities when consuming incoming streams of events to execute continuous computations and provide fast results. Only very few systems have taken into account the combined challenge of executing graph processing on a dynamically evolving graph. However, this is a critical capability as timely computations enable reactive application behaviors upon graph changes. In addition, no existing system supports processing on a live graph and on past version of that evolving graph at the same time. The distinct characteristics of event processing and graph computing, as well as batch processing and live processing yield specific requirements for any computing platform that unifies these approaches. Solutions require (i) data management strategies to keep track of the continuous graph evolution, (ii) appropriate graph processing models that can simultaneously handle computations and graph updates, and (iii) an efficient platform implementation that provides the necessary performance at runtime. To this end, this thesis suggests a combination of an adapted actor model, an event-sourced persistence layer, and a vertex-based, asynchronous live programming model. The event-sourced actor model enables highly concurrent computations in which the full application history is implicitly persisted. This model is then refined into a live graph processing model with a particular focus on asynchronicity, liveness, and parallel execution support. At the same time, the use of event sourcing enables the model to reconstruct global and consistent graph representations from arbitrary points of the timeline. These graph representations form the basis for decoupled, non-live graph processing models. The Chronograph platform represents an implementation of the event-sourced graph model. The platform ingests external update streams and maintains a live graph representation as part of a user-specified graph application. It thus enables live and offline computations on event-driven, history-aware graphs and supports different processing models on the evolving graph. This thesis provides the following contributions: (i) a distributed computing approach with history support based on the actor model and event sourcing, as wall as corresponding and supplementary concepts, (ii) a data management approach for evolving graphs that builds on the event-sourced actor model, (iii) a set of novel and adapted programming and processing models that integrate well with event-sourced graphs, (iv) a distributed platform architecture that implements the event-sourced graph model; and (v) an evaluation framework for such live graph processing systems.

Background and Purpose: The present age of digitalization brings with it progress and new possibilities for health care in general and clinical psychology/psychotherapy in particular. Internet- and mobile-based interventions (IMIs) have often been evaluated. A fully automated version of IMIs are chatbots. Chatbots are automated computer programs that are able to hold, e.g., a script-based conversation with a human being. Chatbots could contribute to the extension of health care services. The aim of this review is to conceptualize the scope and to work out the current state of the art of chatbots fostering mental health. Methods: The present article is a scoping review on chatbots in clinical psychology and psychotherapy. Studies that utilized chatbots to foster mental health were included. Results: The technology of chatbots is still experimental in nature. Studies are most often pilot studies by nature. The field lacks high-quality evidence derived from randomized controlled studies. Results with regard to practicability, feasibility, and acceptance of chatbots to foster mental health are promising but not yet directly transferable to psychotherapeutic contexts. ­Discussion: The rapidly increasing research on chatbots in the field of clinical psychology and psychotherapy requires corrective measures. Issues like effectiveness, sustainability, and especially safety and subsequent tests of technology are elements that should be instituted as a corrective for future funding programs of chatbots in clinical psychology and psychotherapy.

In this chapter, we discuss the privacy implications of mobile sensing and modern psycho-social sciences. We aim to raise awareness of the multifaceted nature of privacy, describing the legal, technical and applied aspects in some detail. Not only since the European GDPR, these aspects lead to a broad spectrum of challenges of which data processors cannot be absolved by a simple consent form from their users. Instead appropriate technical and organizational measures should be put in place through a proper privacy engineering process. Throughout the chapter, we illustrate the importance of privacy protection through a set of examples and also technical approaches to address these challenges. We conclude this chapter with an outlook on privacy in mobile sensing, digital phenotyping and, psychoinformatics.

Slow-running attacks against network applications are often not easy to detect, as the attackers behave according to the specification. The servers of many network applications are not prepared for such attacks, either due to missing countermeasures or because their default configurations ignores such attacks. The pressure to secure network services against such attacks is shifting more and more from the service operators to the network operators of the servers under attack. Recent technologies such as software-defined networking offer the flexibility and extensibility to analyze and influence network flows without the assistance of the target operator. Based on our previous work on a network-based mitigation, we have extended a framework to detect and mitigate slow-running DDoS attacks within the network infrastructure, but without requiring access to servers under attack. We developed and evaluated several identification schemes to identify attackers in the network solely based on network traffic information. We showed that by measuring the packet rate and the uniformity of the packet distances, a reliable identificator can be built, given a training period of the deployment network.

State changes over time are inherent characteristics of stateful applications. So far, there are almost no attempts to make the past application history programmatically accessible or even modifiable. This is primarily due to the complexity of temporal changes and a difficult alignment with prevalent programming primitives and persistence strategies. Retroactive computing enables powerful capabilities though, including computations and predictions of alternate application timelines, post-hoc bug fixes, or retroactive state explorations. We propose an event-driven programming model that is oriented towards serverless computing and applies retroaction to the event sourcing paradigm. Our model is deliberately restrictive, but therefore keeps the complexity of retroactive operations in check. We introduce retro-λ, a runtime platform that implements the model and provides retroactive capabilites to its applications. While retro-λ only shows negligible performance overheads compared to similar solutions for running regular applications, it enables its users to execute retroactive computations on the application histories as part of its programming model.

Distributed event-sourced systems adopt a fairly new architectural style for data-intensive applications that maintains the full history of the application state. However, the performance implications of such systems are not yet well explored, let alone how the performance of these systems can be improved. A central issue is the lack of systematic performance engineering approaches that take into account the specific characteristics of these systems. To address this problem, we suggest a methodology for performance engineering and performance analysis of distributed event-sourced systems based on specific measurements and subsequent, targeted optimizations. The methodology blends in well into existing software engineering processes and helps developers to identify bottlenecks and to resolve performance issues. Using our structured approach, we improved an existing event-sourced system prototype and increased its performance considerably.

Event sourcing is increasingly used and implemented in event-based systems for maintaining the evolution of application state. However, unbounded event logs are impracticable for many systems, as it is difficult to align scalability requirements and long-term runtime behavior with the corresponding storage requirements. To this end, we explore the design space of log pruning approaches suitable for event-sourced systems. Furthermore, we survey specific log pruning mechanisms for event-sourced logs. In a brief evaluation, we point out the trade-offs when applying pruning to event logs and highlight the applicability of log pruning to event-sourced systems.

Stream-based graph systems continuously ingest graph-changing events via an established input stream, performing the required computation on the corresponding graph. While there are various benchmarking and evaluation approaches for traditional, batch-oriented graph processing systems, there are no common procedures for evaluating stream-based graph systems. We, therefore, present GraphTides, a generic framework which includes the definition of an appropriate system model, an exploration of the parameter space, suitable workloads, and computations required for evaluating such systems. Furthermore, we propose a methodology and provide an architecture for running experimental evaluations. With our framework, we hope to systematically support system development, performance measurements, engineering, and comparisons of stream-based graph systems.

Distributed Reflective Denial of Service (DRDoS) attacks are an immanent threat to Internet services. The potential scale of such attacks became apparent in March 2018 when a memcached-based attack peaked at 1.7 Tbps. Novel services built upon UDP increase the need for automated mitigation mechanisms that react to attacks without prior knowledge of the actual application protocols used. With the flexibility that software-defined networks offer, we developed a new approach for defending against DRDoS attacks; it not only protects against arbitrary DRDoS attacks but is also transparent for the attack target and can be used without assistance of the target host operator. The approach provides a robust mitigation system which is protocol-agnostic and effective in the defense against DRDoS attacks.

An increasing number of distributed, event-based systems adopt an architectural style called event sourcing, in which entities keep their entire history in an event log. Event sourcing enables data lineage and allows entities to rebuild any previous state. Restoring previous application states is a straight-forward task in event-sourced systems with a global and totally ordered event log. However, the extraction of causally consistent snapshots from distributed, individual event logs is rendered non-trivial due to causal relationships between communicating entities. High dynamicity of entities increases the complexity of such reconstructions even more. We present approaches for retrospective and global state extraction of event-sourced applications based on distributed event logs. We provide an overview on historical approaches towards distributed debugging and breakpointing, which are closely related to event log-based state reconstruction. We then introduce and evaluate our approach for non-local state extraction from distributed event logs, which is specifically adapted for dynamic and asynchronous event-sourced systems.

Several data-intensive applications take streams of events as a continuous input and internally map events onto a dynamic, graph-based data model which is then used for processing. The differences between event processing, graph computing, as well as batch processing and near-realtime processing yield a number of specific requirements for computing platforms that try to unify theses approaches. By combining an altered actor model, an event-sourced persistence layer, and a vertex-based, asynchronous programming model, we propose a distributed computing platform that supports event-driven, graph-based applications in a single platform. Our Chronograph platform concept enables online and offline computations on event-driven, history-aware graphs and supports different processing models on the evolving graph.

The age of cloud computing has introduced all the mechanisms needed to elastically scale distributed, cloud-enabled applications. At roughly the same time, NoSQL databases have been proclaimed as the scalable alternative to relational databases. Since then, NoSQL databases are a core component of many large-scale distributed applications. This paper evaluates the scalability and elasticity features of the three widely used NoSQL database systems Couchbase, Cassandra and MongoDB under various workloads and settings using throughput and latency as metrics. The numbers show that the three database systems have dramatically different baselines with respect to both metrics and also behave unexpected when scaling out. For instance, while Couchbase's throughput increases by 17% when scaled out from 1 to 4 nodes, MongoDB's throughput decreases by more than 50%. These surprising results show that not all tested NoSQL databases do scale as expected and even worse, in some cases scaling harms performances.

Many data-driven applications require mechanisms for processing interconnected or graph-based data sets. Several platforms exist for offline processing of such data and fewer solutions address online computations on dynamic graphs. We combined a modified actor model, an event-sourced persistence layer, and a vertex-based, asynchronous programming model in order to unify event-driven and graph-based computations. Our distributed chronograph platform supports both near-realtime and batch computations on dynamic, event-driven graph topologies, and enables full history tracking of the evolving graphs over time.

There are many high-speed TCP variants with different congestion control algorithms, which are designed for specific settings or use cases. Distinct features of these algorithms are meant to optimize different aspects of network performance, and the choice of TCP variant strongly influences application performance. However, setting up tests to help with the decision of which variant to use can be problematic, as many systems are not designed to deal with high bandwidths, such as 10 Gbps or more. This paper provides an overview of pitfalls and challenges of realistic network analysis to help in the decision making process.

A basic reason for backend systems in mobile application architectures is the centralized management of state. Mobile clients synchronize local states with the backend in order to maintain an up-to-date view of the application state. As not all mobile social applications require strong consistency guarantees, we survey an alternative approach using special data structures for mobile applications. These data structures only provide eventual consistency, but allow for conflict-free replication between peers. Our analysis collects the requirements of social mobile applications for being suitable for this approach. Based on exemplary mobile social applications, we also point out the benefits of serverless architecture or architectures with a thin backend layer.

Privacy strategies and privacy patterns are fundamental concepts of the privacy-by-design engineering approach. While they support a privacy-aware development process for IT systems, the concepts used by malicious, privacy-threatening parties are generally less understood and known. We argue that understanding the “dark side”, namely how personal data is abused, is of equal importance. In this paper, we introduce the concept of privacy dark strategies and privacy dark patterns and present a framework that collects, documents, and analyzes such malicious concepts. In addition, we investigate from a psychological perspective why privacy dark strategies are effective. The resulting framework allows for a better understanding of these dark concepts, fosters awareness, and supports the development of countermeasures. We aim to contribute to an easier detection and successive removal of such approaches from the Internet to the benefit of its users.

The actor model is an established programming model for distributed applications. Combining event sourcing with the actor model allows the reconstruction of previous states of an actor. When this event sourcing approach for actors is enhanced with additional causality information, novel types of actor-based, retroactive computations are possible. A globally consistent state of all actors can be reconstructed retrospectively. Even retroactive changes of actor behavior, state, or messaging are possible, with partial recomputations and projections of changes in the past. We believe that this approach may provide beneficial features to actor-based systems, including retroactive bugfixing of applications, decoupled asynchronous global state reconstruction for recovery, simulations, and exploration of distributed applications and algorithms.

In large-scale disaster scenarios, efficient triage management is a major challenge for emergency services. Rescue forces traditionally respond to such incidents with a paper-based triage system, but technical solutions can potentially achieve improved usability and data availability. We develop a triage management system based on commodity hardware and software components to verify this claim. We use a single-hop, ad-hoc network architecture with multi-master replication, a tablet-based device setup, and a mobile application for emergency services. We study our system in cooperation with regional emergency services and report on experiences from a field exercise. We show that state-of-the-art commodity technology provides the means necessary to implement a triage management system compatible with existing emergency service procedures, while introducing additional benefits. This work highlights that powerful real-world ad-hoc networking applications do not require unreasonable development effort, as existing tools from distributed systems, such as replicating NoSQL databases, can be used successfully.

The increasing availability of 10G Ethernet network capabilities challenges existing transport layer protocols. As 10G connections gain momentum outside of backbone networks, the choice of appropriate TCP congestion control algorithms becomes even more relevant for networked applications running in environments such as data centers. Therefore, we provide an extensive overview of relevant TCP congestion control algorithms for high-speed environments leveraging 10G. We analyzed and evaluated six TCP variants using a physical network testbed, with a focus on the effects of propagation delay and significant drop rates. The results indicate that of the algorithms compared, BIC is most suitable when no legacy variant is present, CUBIC is suggested otherwise.

The processing of large-scale data sets and streaming data is challenging traditional computing platforms and lacks increasingly relevant features such as data lineage and inherent support for retrospective and predictive analytics. By combining concepts from event processing and graph computing, an Actor-related programming model, and an event-based, time-aware persistence approach into a unified distributed processing solution, we suggest a novel processing approach that embraces the idea of graph-based computing with built-in support for application history.

Systems for highly interconnected application domains are increasingly taking advantage of graph-based computing platforms. Existing platforms employ a batch-oriented computing model and neglect near-realtime processing or temporal analysis. We suggest an extended conceptual model for event-driven computing on graphs. It takes into account the evolution of a graph and enables temporal analyses, processing on previous graph states, and retroactive modifications.

Distributed database systems represent an essential component of modern enterprise application architectures. If the overall application needs to provide reliability and availability, the database has to guarantee these properties as well. Entailing non-functional database features such as replication, consistency, conflict management, and partitioning represent subsequent challenges for successfully designing and operating an available and reliable database system. In this document, we identify why these concepts are important for databases and classify their design options. Moreover, we survey how eleven modern database systems implement these reliability and availability properties.

Computational power for content filtering in high-speed networks reaches a limit, but many applications as intrusion detection systems rely on such processes. Especially signature based methods need extraction of header fields. Hence we created an parallel protocol-stack parser module on the NetFPGA 10G architecture with a framework for simple adaption to custom protocols. Our measurements prove that the appliance operates at 9.5 Gb/s with a delay in order of any active hop. The work provides the foundation to use for application specific projects in the NetFPGA context.

Modern web applications are concurrently used by many users and provide increasingly interactive features. Multi-core processors, highly distributed backend architectures, and new web technologies force a reconsideration of approaches for concurrent programming in order to fulfil scalability demands and to implement modern web application features. We provide a survey on different concepts and techniques of concurrency inside web architectures and guide through viable concurrency alternatives for architects and developers.

Discrete event simulations (DES) represent the status quo for many different types of simulations. There are still open challenges, such as designing distributed simulation architectures, providing development and debugging support, or analyzing and evaluating simulation runs. In the area of scalable, distributed application architectures exists an architectural style called event sourcing, which shares the same inherent idea as DES. We believe that both approaches can benefit from each other and provide a comparison of both approaches. Next, we point out how event sourcing concepts can address DES issues. Finally, we suggest a hybrid architecture that allows to mutually execute simulations and real applications, enabling seamless transitions between both.

The goal of the diretto project is the creation of an extensible infrastructure and easy-to-use toolset for distributed on-site media reporting and collaborative event coverage in real-time. It empowers collocated users to participate dynamicallyin event reporting, andfacilitatescollaboration with remote users. For example, to cover public events or support disaster relief missions with on-site information. The diretto platform focuses on scalability to support large crowd participation. Our platform currently supports smartphone clients, areportingsolution for SLR cameras, and a rich web application for remote collaborators. diretto is easily extensible and can be tailored to mission-specific requirements.