TY - JOUR A1 - Grohmann, Johannes A1 - Herbst, Nikolas A1 - Chalbani, Avi A1 - Arian, Yair A1 - Peretz, Noam A1 - Kounev, Samuel T1 - A Taxonomy of Techniques for SLO Failure Prediction in Software Systems JF - Computers N2 - Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research. KW - taxonomy KW - survey KW - failure prediction KW - anomaly prediction KW - anomaly detection KW - self-aware computing KW - self-adaptive systems KW - performance prediction Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200594 SN - 2073-431X VL - 9 IS - 1 ER - TY - THES A1 - Reul, Christian T1 - An Intelligent Semi-Automatic Workflow for Optical Character Recognition of Historical Printings T1 - Ein intelligenter semi-automatischer Workflow für die OCR historischer Drucke N2 - Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, Automatic Text Recognition (ATR) and postcorrection. Their major drawback is that they only offer limited applicability by non-technical users like humanist scholars, in particular when it comes to the combined use of several tools in a workflow. Furthermore, depending on the material, these tools are usually not able to fully automatically achieve sufficiently low error rates, let alone perfect results, creating a demand for an interactive postcorrection functionality which, however, is generally not incorporated. This thesis addresses these issues by presenting an open-source OCR software called OCR4all which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required Ground Truth (GT) for training stronger mixed models (for segmentation as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers better recognition capabilities in combination with a very comfortable Graphical User Interface (GUI) that allows error corrections not only in the final output, but already in early stages to minimize error propagation. In the long run this constant manual correction produces large quantities of valuable, high quality training material which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. The architecture of OCR4all allows for an easy integration (or substitution) of newly developed tools for its main components by supporting standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings. In addition to OCR4all, several methodical extensions in the form of accuracy improving techniques for training and recognition are presented. Most notably an effective, sophisticated, and adaptable voting methodology using a single ATR engine, a pretraining procedure, and an Active Learning (AL) component are proposed. Experiments showed that combining pretraining and voting significantly improves the effectiveness of book-specific training, reducing the obtained Character Error Rates (CERs) by more than 50%. The proposed extensions were further evaluated during two real world case studies: First, the voting and pretraining techniques are transferred to the task of constructing so-called mixed models which are trained on a variety of different fonts. This was done by using 19th century Fraktur script as an example, resulting in a considerable improvement over a variety of existing open-source and commercial engines and models. Second, the extension from ATR on raw text to the adjacent topic of typography recognition was successfully addressed by thoroughly indexing a historical lexicon that heavily relies on different font types in order to encode its complex semantic structure. During the main experiments on very complex early printed books even users with minimal or no experience were able to not only comfortably deal with the challenges presented by the complex layout, but also to recognize the text with manageable effort and great quality, achieving excellent CERs below 0.5%. Furthermore, the fully automated application on 19th century novels showed that OCR4all (average CER of 0.85%) can considerably outperform the commercial state-of-the-art tool ABBYY Finereader (5.3%) on moderate layouts if suitably pretrained mixed ATR models are available. N2 - Die Optische Zeichenerkennung (Optical Character Recognition, OCR) auf historischen Drucken stellt nach wie vor eine große Herausforderung dar, hauptsächlich aufgrund des häufig komplexen Layouts und der hoch varianten Typographie. In den letzten Jahre gab es große Fortschritte im Bereich der historischen OCR, die nicht selten auch in Form von Open Source Tools interessierten Nutzenden frei zur Verfügung stehen. Der Nachteil dieser Tools ist, dass sie meist ausschließlich über die Kommandozeile bedient werden können und somit nicht-technische Nutzer schnell überfordern. Außerdem sind die Tools häufig nicht aufeinander abgestimmt und verfügen dementsprechend nicht über gemeinsame Schnittstellen. Diese Arbeit adressiert diese Problematik mittels des Open Source Tools OCR4all, das verschiedene State-of-the-Art OCR Lösungen zu einem zusammenhängenden Workflow kombiniert und in einer einzigen Anwendung kapselt. Besonderer Wert liegt dabei darauf, auch nicht-technischen Nutzern zu erlauben, selbst die ältesten und anspruchsvollen Drucke selbstständig und mit höchster Qualität zu erfassen. OCR4all ist vollständig über eine komfortable graphische Nutzeroberfläche bedienbar und bietet umfangreiche Möglichkeiten hinsichtlich Konfiguration und interaktiver Nachkorrektur. Zusätzlich zu OCR4all werden mehrere methodische Erweiterungen präsentiert, um die Effektivität und Effizienz der Trainings- und Erkennungsprozesse zur Texterkennung zu optimieren. Während umfangreicher Evaluationen konnte gezeigt werden, dass selbst Nutzer ohne nennenswerte Vorerfahrung in der Lage waren, OCR4all eigenständig auf komplexe historische Drucke anzuwenden und dort hervorragende Zeichenfehlerraten von durchschnittlich unter 0,5% zu erzielen. Die methodischen Verbesserungen mit Blick auf die Texterkennung reduzierten dabei die Fehlerrate um über 50% im Vergleich zum etablierten Standardansatz. KW - Optische Zeichenerkennung KW - Optical Character Recognition KW - Document Analysis KW - Historical Printings KW - Alter Druck Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-209239 ER - TY - JOUR A1 - Krupitzer, Christian A1 - Temizer, Timur A1 - Prantl, Thomas A1 - Raibulet, Claudia T1 - An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things JF - IEEE Access N2 - The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study. KW - Design patterns KW - Internet of Things KW - IoT KW - self-adaptive systems KW - software engineering Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229984 VL - 8 ER - TY - RPRT A1 - Grigorjew, Alexej A1 - Metzger, Florian A1 - Hoßfeld, Tobias A1 - Specht, Johannes A1 - Götz, Franz-Josef A1 - Chen, Feng A1 - Schmitt, Jürgen T1 - Asynchronous Traffic Shaping with Jitter Control N2 - Asynchronous Traffic Shaping enabled bounded latency with low complexity for time sensitive networking without the need for time synchronization. However, its main focus is the guaranteed maximum delay. Jitter-sensitive applications may still be forced towards synchronization. This work proposes traffic damping to reduce end-to-end delay jitter. It discusses its application and shows that both the prerequisites and the guaranteed delay of traffic damping and ATS are very similar. Finally, it presents a brief evaluation of delay jitter in an example topology by means of a simulation and worst case estimation. KW - Echtzeit KW - Rechnernetz KW - Latenz KW - Ethernet KW - TSN KW - jitter KW - traffic damping Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205824 ER - TY - JOUR A1 - Frey, Anna A1 - Gassenmaier, Tobias A1 - Hofmann, Ulrich A1 - Schmitt, Dominik A1 - Fette, Georg A1 - Marx, Almuth A1 - Heterich, Sabine A1 - Boivin-Jahns, Valérie A1 - Ertl, Georg A1 - Bley, Thorsten A1 - Frantz, Stefan A1 - Jahns, Roland A1 - Störk, Stefan T1 - Coagulation factor XIII activity predicts left ventricular remodelling after acute myocardial infarction JF - ESC Heart Failure N2 - Aims Acute myocardial infarction (MI) is the major cause of chronic heart failure. The activity of blood coagulation factor XIII (FXIIIa) plays an important role in rodents as a healing factor after MI, whereas its role in healing and remodelling processes in humans remains unclear. We prospectively evaluated the relevance of FXIIIa after acute MI as a potential early prognostic marker for adequate healing. Methods and results This monocentric prospective cohort study investigated cardiac remodelling in patients with ST-elevation MI and followed them up for 1 year. Serum FXIIIa was serially assessed during the first 9 days after MI and after 2, 6, and 12 months. Cardiac magnetic resonance imaging was performed within 4 days after MI (Scan 1), after 7 to 9 days (Scan 2), and after 12 months (Scan 3). The FXIII valine-to-leucine (V34L) single-nucleotide polymorphism rs5985 was genotyped. One hundred forty-six patients were investigated (mean age 58 ± 11 years, 13% women). Median FXIIIa was 118 % (quartiles, 102–132%) and dropped to a trough on the second day after MI: 109%(98–109%; P < 0.001). FXIIIa recovered slowly over time, reaching the baseline level after 2 to 6 months and surpassed baseline levels only after 12 months: 124 % (110–142%). The development of FXIIIa after MI was independent of the genotype. FXIIIa on Day 2 was strongly and inversely associated with the relative size of MI in Scan 1 (Spearman’s ρ = –0.31; P = 0.01) and Scan 3 (ρ = –0.39; P < 0.01) and positively associated with left ventricular ejection fraction: ρ = 0.32 (P < 0.01) and ρ = 0.24 (P = 0.04), respectively. Conclusions FXIII activity after MI is highly dynamic, exhibiting a significant decline in the early healing period, with reconstitution 6 months later. Depressed FXIIIa early after MI predicted a greater size of MI and lower left ventricular ejection fraction after 1 year. The clinical relevance of these findings awaits to be tested in a randomized trial. KW - blood coagulation factor XIII KW - ST-elevation myocardial infarction KW - healing and remodelling processes KW - cardiac magnetic resonance imaging Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236013 VL - 7 IS - 5 ER - TY - RPRT A1 - Metzger, Florian T1 - Crowdsensed QoE for the community - a concept to make QoE assessment accessible N2 - In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own. KW - Quality of Experience KW - Crowdsourcing KW - Crowdsensing KW - QoE Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203748 N1 - Originally written in 2017, but never published. ER - TY - JOUR A1 - Du, Shitong A1 - Lauterbach, Helge A. A1 - Li, Xuyou A1 - Demisse, Girum G. A1 - Borrmann, Dorit A1 - Nüchter, Andreas T1 - Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration JF - Sensors N2 - Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences. KW - mapping KW - continuous-time SLAM KW - deformation-based method KW - time calibration Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219988 SN - 1424-8220 VL - 20 IS - 23 ER - TY - THES A1 - Borchers, Kai T1 - Decentralized and Pulse-based Clock Synchronization in SpaceWire Networks for Time-triggered Data Transfers T1 - Dezentralisierte und Puls-basierte Uhrensynchronisation in SpaceWire Netzwerken für zeitgesteuerten Datentransfer N2 - Time-triggered communication is widely used throughout several industry do- mains, primarily for reliable and real-time capable data transfers. However, existing time-triggered technologies are designed for terrestrial usage and not directly applicable to space applications due to the harsh environment. In- stead, specific hardware must be developed to deal with thermal, mechanical, and especially radiation effects. SpaceWire, as an event-triggered communication technology, has been used for years in a large number of space missions. Its moderate complexity, her- itage, and transmission rates up to 400 MBits/s are one of the main ad- vantages and often without alternatives for on-board computing systems of spacecraft. At present, real-time data transfers are either achieved by prior- itization inside SpaceWire routers or by applying a simplified time-triggered approach. These solutions either imply problems if they are used inside dis- tributed on-board computing systems or in case of networks with more than a single router are required. This work provides a solution for the real-time problem by developing a novel clock synchronization approach. This approach is focused on being compatible with distributed system structures and allows time-triggered data transfers. A significant difference to existing technologies is the remote clock estimation by the use of pulses. They are transferred over the network and remove the need for latency accumulation, which allows the incorporation of standardized SpaceWire equipment. Additionally, local clocks are controlled decentralized and provide different correction capabilities in order to handle oscillator induced uncertainties. All these functionalities are provided by a developed Network Controller (NC), able to isolate the attached network and to control accesses. N2 - Zeitgesteuerte Datenübertragung ist in vielen Industriezweigen weit verbreitet, primär für zuverlässige und echtzeitfähige Kommunikation. Bestehende Technologien sind jedoch für den terrestrischen Gebrauch konzipiert und aufgrund der rauen Umgebung nicht direkt auf Weltraumanwendungen anwendbar. Stattdessen wird spezielle Hardware entwickelt, um Strahlungseffekten zu widerstehen sowie thermischen und mechanischen Belastungen standzuhalten. SpaceWire wurde als ereignisgesteuerte Kommunikationstechnologie entwickelt und wird seit Jahren in einer Vielzahl von Weltraummissionen verwendet. Dessen erfolgreiche Verwendung, überschaubare Komplexität, und Übertragungsraten bis zu 400 MBit/s sind einige seiner Hauptvorteile. Derzeit werden Datenübertragungen in Echtzeit entweder durch Priorisierung innerhalb von SpaceWire Router erreicht, oder durch Anwendung von vereinfachten zeitgesteuerten Ansätzen. Diese Lösungen implizieren entweder Probleme in verteilten Systemarchitekturen oder in SpaceWire Netzwerken mit mehreren Routern. Diese Arbeit beschreibt eine Uhrensynchronisation, die bestimmte Eigenschaften von SpaceWire ausnutzt, um das Echtzeitproblem zu lösen. Der Ansatz ist dabei kompatibel mit verteilten Systemstrukturen und ermöglicht eine zeitgesteuerte Datenübertragung. KW - Datenübertragung KW - Field programmable gate array KW - FPGA KW - Formal verification KW - SpaceWire KW - Communication KW - Raumfahrttechnik KW - Verifikation KW - Hardware Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-215606 ER - TY - THES A1 - Hofmann, Jan T1 - Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking T1 - Deep Reinforcement Learning zur Konfiguration von Time-Sensitive-Networking N2 - Reliable, deterministic real-time communication is fundamental to most industrial systems today. In many other domains Ethernet has become the most common platform for communication networks, but has been unsuitable to satisfy the requirements of industrial networks for a long time. This has changed with the introduction of Time-Sensitive-Networking (TSN), a set of standards utilizing Ethernet to implement deterministic real-time networks. This makes Ethernet a viable alternative to the expensive fieldbus systems commonly used in industrial environments. However, TSN is not a silver bullet. Industrial networks are a complex and highly dynamic environment and the configuration of TSN, especially with respect to latency, is a challenging but crucial task. Various approaches have been pursued for the configuration of TSN in dynamic industrial environments. Optimization techniques like Linear Programming (LP) are able to determine an optimal configuration for a given network, but the time consumption exponentially increases with the complexity of the environment. Machine Learning (ML) has become widely popular in the last years and is able to approximate a near-optimal TSN configuration for networks of different complexity. Yet, ML models are usually trained in a supervised manner which requires large amounts of data that have to be generated for the specific environment. Therefore, supervised methods are not scalable and do not adapt to changing dynamics of the network environment. To address these issues, this work proposes a Deep Reinforcement Learning (DRL) approach to the configuration of TSN in industrial networks. DRL combines two different disciplines, Deep Learning (DL) and Reinforcement Learning (RL), and has gained considerable traction in the last years due to breakthroughs in various domains. RL is supposed to autonomously learn a challenging task like the configuration of TSN without requiring any training data. The addition of DL allows to apply well-studied RL methods to a complex environment such as dynamic industrial networks. There are two major contributions made in this work. In the first step, an interactive environment is proposed which allows for the simulation and configuration of industrial networks using basic TSN mechanisms. The environment provides an interface that allows to apply various DRL methods to the problem of TSN configuration. The second contribution of this work is an in-depth study on the application of two fundamentally different DRL methods to the proposed environment. Both methods are evaluated on networks of different complexity and the results are compared to the ground truth and to the results of two supervised ML approaches. Ultimately, this work investigates if DRL can adapt to changing dynamics of the environment in a more scalable manner than supervised methods. N2 - Zuverlässige Echtzeitnetzwerke spielen eine zentrale Rolle im heutigen industriellen Umfeld. Während sich in anderen Anwendungsbereichen Ethernet als Technik für Kommunikationsnetze durchsetzen konnte, basiert industrielle Kommunikation bis heute häufig noch auf teuren Feldbus-Systemen. Mit der Einführung von Time-Sensitive-Networking (TSN) wurde Ethernet schließlich um eine Reihe von Standards erweitert, die die hohen Anforderungen an Echtzeitkommunikation erfüllen und Ethernet damit auch im industriellen Umfeld etablieren sollen. Doch für eine zuverlässige Kommunikation, besonders im Hinblick auf die Übertragungsverzögerung von Datenpaketen (Latenz), ist die richtige Konfiguration von TSN entscheidend. Dynamische Netzwerke zu konfigurieren ist ein Optimierungsproblem, das verschiedene Herausforderungen birgt. Verfahren wie die lineare Optimierung liefern zwar optimale Ergebnisse, jedoch steigt der Zeitaufwand exponentiell mit der Größe der Netzwerke. Moderne Lösungsansätze wie Machine Learning (ML) können sich einer optimalen Lösung annähern, benötigen jedoch üblicherweise große Datenmengen, auf denen sie trainiert werden (Supervised Learning). Diese Arbeit untersucht die Anwendung von Deep Reinforcement Learning (DRL) zur Konfiguration von TSN. DRL kombiniert Reinforcement Learning (RL), also das selbstständige Lernen ausschließlich durch Interaktion, mit dem Deep Learning (DL), dem Lernen mittels tiefer neuronaler Netze. Die Arbeit beschreibt, wie sich eine Umgebung für DRL zur Simulation und Konfiguration von industriellen Netzwerken implementieren lässt, und untersucht die Anwendung zweier unterschiedlicher Ansätze von DRL auf das Problem der TSN-Konfiguration. Beide Methoden wurden anhand von zwei unterschiedlich komplexen Datensätzen ausgewertet und die Ergebnisse sowohl mit den zeitaufwändig generierten Optimallösungen als auch mit den Ergebnissen zweier Supervised Learning-Ansätze verglichen. Es konnte gezeigt werden, dass DRL optimale Ergebnisse auf kleinen Netzwerken erzielen kann und insgesamt in der Lage ist, Supervised Learning bei der Konfiguration von TSN zu übertreffen. Weiterhin konnte in der Arbeit demonstriert werden, dass sich DRL schnell an fundamentale Veränderungen der Umgebung anpassen kann, was mit Supervised Learning nur durch deutlichen Mehraufwand möglich ist. KW - Reinforcement Learning KW - Time-Sensitive Networking KW - Deep Reinforcement Learning KW - Time-Sensitive-Networking KW - Real-Time-Networks KW - Bestärkendes Lernen KW - Echtzeit-Netzwerke Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-215953 ER - TY - JOUR A1 - Hoßfeld, Tobias A1 - Heegaard, Poul E. A1 - Skrorin-Kapov, Lea A1 - Varela, Martín T1 - Deriving QoE in systems: from fundamental relationships to a QoE-based Service-level Quality Index JF - Quality and User Experience N2 - With Quality of Experience (QoE) research having made significant advances over the years, service and network providers aim at user-centric evaluation of the services provided in their system. The question arises how to derive QoE in systems. In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to varying distributions of user rating scores for different test conditions. Such models are commonly exploited by providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question then becomes how to combine (a) user rating distributions obtained from subjective studies, and (b) system parameter distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove fundamental relationships for the derivation of QoE in systems, thus providing an important link between the QoE community and the systems community. In our numerical examples, we focus mainly on QoE metrics. We furthermore provide a more generalized view on quantifying the quality of systems by defining a QoE-based Service-level Quality Index. This index exploits the fact that quality can be seen as a proxy measure for utility. Following the assumption that not all user sessions should be weighted equally, we aim to provide a generic framework that can be utilized to quantify the overall utility of a service delivered by a system. KW - QoE fundamentals KW - Expected QoE KW - Expected MOS KW - Good-or-Better (GoB) KW - QoS-QoE mapping functions KW - Service-level Quality Index (SQI) Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-235597 SN - 2366-0139 VL - 5 ER -