05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Prozessbausteine(2014) Eberle, Hanna; Leymann, Frank (Prof. Dr.)Gegenwärtig existierenden Modellierungssprachen und Werkzeugen zur Umsetzung prozessbasierter Anwendungen liegt im Allgemeinen die Annahme eines zur Entwicklungszeit bekannten und in seiner Struktur vollständig ausmodellierten Prozessmodells zugrunde. Für Szenarien, in welchen eine prozessbasierte Anwendung neben stabilen, d.h. zur Modellierungszeit des Prozesses bekannten, auch durch dynamische, d.h. erst zur Anwendungslaufzeit geltende, Rahmenbedingungen beeinflusst wird, ist eine derartige statische Prozessmodellierung nur bedingt geeignet. In diesen Szenarien ist es vielmehr wünschenswert, (i) zur Entwicklungszeit bereits bekannte Prozessteile der Anwendung detailliert ausmodellieren zu können, und diese (ii) zur Laufzeit der Anwendung unter Berücksichtigung der zum Ausführungszeitpunkt geltenden dynamischen Rahmenbedingungen zum vollständigen Prozess der Anwendung zu integrieren. Das im Verlauf dieser Arbeit vorgestellte Konzept der Prozessbausteine setzt an diesem Punkt an und schafft ein Rahmenwerk für die Modellierung und Ausführung prozessbasierter Anwendungen unter Berücksichtigung sowohl stabiler als auch dynamischer Rahmenbedingungen. Kerngedanke des Konzepts ist die Abbildung stabiler Rahmenbedingungen zur Entwicklungszeit in Form teilweise unvollständiger Prozessmodelle, sogenannter Prozessbausteine. Zu einem späteren Zeitpunkt im Lebenszyklus der Anwendung werden diese Prozessbausteine dann, motiviert durch die jeweils geltenden dynamischen Rahmenbedingungen, mit weiteren Prozessbausteinen zum vollständigen Prozessmodell der Anwendung integriert. Zur vollständigen Unterstützung der Entwicklung von Anwendungen auf Grundlage dieses Konzepts umfasst die vorliegende Arbeit die Definition eines Metamodells für sowohl die Modellierung einzelner als auch die Repräsentation integrierter Prozessbausteine, die Beschreibung der Ausführung integrierter Prozessbausteine, sowie die Vorstellung einer Architektur für die Ausführung integrierter Prozessbausteine.Item Open Access Modeling the interface between morphology and syntax in data-driven dependency parsing(2016) Seeker, Wolfgang; Kuhn, Jonas (Prof. Dr.)When people formulate sentences in a language, they follow a set of rules specific to that language that defines how words must be put together in order to express the intended meaning. These rules are called the grammar of the language. Languages have essentially two ways of encoding grammatical information: word order or word form. English uses primarily word order to encode different meanings, but many other languages change the form of the words themselves to express their grammatical function in the sentence. These languages are commonly subsumed under the term morphologically rich languages. Parsing is the automatic process for predicting the grammatical structure of a sentence. Since grammatical structure guides the way we understand sentences, parsing is a key component in computer programs that try to automatically understand what people say and write. This dissertation is about parsing and specifically about parsing languages with a rich morphology, which encode grammatical information in the form of words. Today’s parsing models for automatic parsing were developed for English and achieve good results on this language. However, when applied to other languages, a significant drop in performance is usually observed. The standard model for parsing is a pipeline model that separates the parsing process into different steps, in particular it separates the morphological analysis, i.e. the analysis of word forms, from the actual parsing step. This dissertation argues that this separation is one of the reasons for the performance drop of standard parsers when applied to other languages than English. An analysis is presented that exposes the connection between the morphological system of a language and the errors of a standard parsing model. In a second series of experiments, we show that knowledge about the syntactic structure of sentence can support the prediction of morphological information. We then argue for an alternative approach that models morphological analysis and syntactic analysis jointly instead of separating them. We support this argumentation with empirical evidence by implementing two parsers that model the relationship between morphology and syntax in two different but complementary ways.Item Open Access Computational modelling of coreference and bridging resolution(2019) Rösiger, Ina; Kuhn, Jonas (Prof. Dr.)Item Open Access Interacting with large high-resolution display workplaces(2018) Lischke, Lars; Schmidt, Albrecht (Prof.)Large visual spaces provide a unique opportunity to communicate large and complex pieces of information; hence, they have been used for hundreds of years for varied content including maps, public notifications and artwork. Understanding and evaluating complex information will become a fundamental part of any office work. Large high-resolution displays (LHRDs) have the potential to further enhance the traditional advantages of large visual spaces and combine them with modern computing technology, thus becoming an essential tool for understanding and communicating data in future office environments. For successful deployment of LHRDs in office environments, well-suited interaction concepts are required. In this thesis, we build an understanding of how concepts for interaction with LHRDs in office environments could be designed. From the human-computer interaction (HCI) perspective three aspects are fundamental: (1) The way humans perceive and react to large visual spaces is essential for interaction with content displayed on LHRDs. (2) LHRDs require adequate input techniques. (3) The actual content requires well-designed graphical user interfaces (GUIs) and suitable input techniques. Perceptions influence how users can perform input on LHRD setups, which sets boundaries for the design of GUIs for LHRDs. Furthermore, the input technique has to be reflected in the design of the GUI. To understand how humans perceive and react to large visual information on LHRDs, we have focused on the influence of visual resolution and physical space. We show that increased visual resolution has an effect on the perceived media quality and the perceived effort and that humans can overview large visual spaces without being overwhelmed. When the display is wider than 2 m users perceive higher physical effort. When multiple users share an LHRD, they change their movement behavior depending whether a task is collaborative or competitive. For building LHRDs consideration must be given to the increased complexity of higher resolutions and physically large displays. Lower screen resolutions provide enough display quality to work efficiently, while larger physical spaces enable users to overview more content without being overwhelmed. To enhance user input on LHRDs in order to interact with large information pieces, we built working prototypes and analyzed their performance in controlled lab studies. We showed that eye-tracking based manual and gaze input cascaded (MAGIC) pointing can enhance target pointing to distant targets. MAGIC pointing is particularly beneficial when the interaction involves visual searches between pointing to targets. We contributed two gesture sets for mid-air interaction with window managers on LHRDs and found that gesture elicitation for an LHRD was not affected by legacy bias. We compared shared user input on an LHRD with personal tablets, which also functioned as a private working space, to collaborative data exploration using one input device together for interacting with an LHRD. The results showed that input with personal tablets lowered the perceived workload. Finally, we showed that variable movement resistance feedback enhanced one-dimensional data input when no visual input feedback was provided. We concluded that context-aware input techniques enhance the interaction with content displayed on an LHRD so it is essential to provide focus for the visual content and guidance for the user while performing input. To understand user expectations of working with LHRDs we prototyped with potential users how an LHRD work environment could be designed focusing on the physical screen alignment and the placement of content on the display. Based on previous work, we implemented novel alignment techniques for window management on LHRDs and compared them in a user study. The results show that users prefer techniques, that enhance the interaction without breaking well-known desktop GUI concepts. Finally, we provided the example of how an application for browsing scientific publications can benefit from extended display space. Overall, we show that GUIs for LHRDs should support the user more strongly than GUIs for smaller displays to arrange content meaningful or manage and understand large data sets, without breaking well-known GUI-metaphors. In conclusion, this thesis adopts a holistic approach to interaction with LHRDs in office environments. Based on enhanced knowledge about user perception of large visual spaces, we discuss novel input techniques for advanced user input on LHRDs. Furthermore, we present guidelines for designing future GUIs for LHRDs. Our work creates the design space of LHRD workplaces and identifies challenges and opportunities for the development of future office environments.Item Open Access Partnerübergreifende Geschäftsprozesse und ihre Realisierung in BPEL(2016) Kopp, Oliver; Leymann, Frank (Prof. Dr. Dr. h. c.)Diese Arbeit beschäftigt sich mit Geschäftsprozessen, die die Grenzen von Organisationen überspannen. Solche Geschäftsprozesse werden Choreographien genannt. In der Arbeit wird die CREAM-Methode vorgestellt, die zeigt, wie Choreographien modelliert werden können. Im Gegensatz zu Choreographien bezeichnen Orchestrierungen ausführbare Geschäftsprozesse einer einzelnen Organisation, die Dienste nutzen, um ein Geschäftsziel zu erreichen. Eine Variante der CREAM-Methode erlaubt, von einer Orchestrierung durch Aufteilung der Orchestrierung eine Choreographie zu erhalten. Um hierbei die impliziten orchestrierungsinternen Datenabhängigkeiten in Nachrichtenaustausche zu transformieren, wird der explizite Datenfluss der Orchestrierung benötigt. Die Web Services Business Process Execution Language (BPEL) ist eine verbreitete Sprache zur Modellierung von Geschäftsprozessen. In ihr wird der Datenfluss implizit modelliert und somit wird ein Verfahren benötigt, das den expliziten Datenfluss bestimmt. In dieser Arbeit wird ein solches Verfahren vorgestellt. Um eine Choreographie zu modellieren, wird eine Choreographiesprache benötigt. Zur Identifikation einer geeigneten Sprache werden in dieser Arbeit Kriterien zur Evaluation von Choreographiesprachen vorgestellt und damit Choreographiesprachen im Web-Service-Umfeld bewertet. Da keine der betrachteten Sprachen alle Kriterien erfüllt, wird die Sprache BPEL4Chor vorgestellt, die alle Kriterien erfüllt. Um die wohldefinierte Ausführungssemantik von BPEL wiederzuverwenden, verwendet BPEL4Chor die Sprache BPEL als Beschreibungssprache des Verhaltens jedes Teilnehmers in der Choreographie. BPEL4Chor verwendet analog zu BPEL XML als Serialisierungsformat und spezifiziert keine eigene graphische Repräsentation. Die Business Process Modeling Notation (BPMN) ist der de-facto Standard, um Geschäftsprozesse graphisch darzustellen. Deshalb wird in dieser Arbeit BPMN so erweitert, dass alle in BPEL4Chor verfügbaren Konstrukte mittels BPMN modelliert werden können.Item Open Access Efficient fault tolerance for selected scientific computing algorithms on heterogeneous and approximate computer architectures(2018) Schöll, Alexander; Wunderlich, Hans-Joachim (Prof. Dr.)Scientific computing and simulation technology play an essential role to solve central challenges in science and engineering. The high computational power of heterogeneous computer architectures allows to accelerate applications in these domains, which are often dominated by compute-intensive mathematical tasks. Scientific, economic and political decision processes increasingly rely on such applications and therefore induce a strong demand to compute correct and trustworthy results. However, the continued semiconductor technology scaling increasingly imposes serious threats to the reliability and efficiency of upcoming devices. Different reliability threats can cause crashes or erroneous results without indication. Software-based fault tolerance techniques can protect algorithmic tasks by adding appropriate operations to detect and correct errors at runtime. Major challenges are induced by the runtime overhead of such operations and by rounding errors in floating-point arithmetic that can cause false positives. The end of Dennard scaling induces central challenges to further increase the compute efficiency between semiconductor technology generations. Approximate computing exploits the inherent error resilience of different applications to achieve efficiency gains with respect to, for instance, power, energy, and execution times. However, scientific applications often induce strict accuracy requirements which require careful utilization of approximation techniques. This thesis provides fault tolerance and approximate computing methods that enable the reliable and efficient execution of linear algebra operations and Conjugate Gradient solvers using heterogeneous and approximate computer architectures. The presented fault tolerance techniques detect and correct errors at runtime with low runtime overhead and high error coverage. At the same time, these fault tolerance techniques are exploited to enable the execution of the Conjugate Gradient solvers on approximate hardware by monitoring the underlying error resilience while adjusting the approximation error accordingly. Besides, parameter evaluation and estimation methods are presented that determine the computational efficiency of application executions on approximate hardware. An extensive experimental evaluation shows the efficiency and efficacy of the presented methods with respect to the runtime overhead to detect and correct errors, the error coverage as well as the achieved energy reduction in executing the Conjugate Gradient solvers on approximate hardware.Item Open Access Eine Methode zum Verteilen, Adaptieren und Deployment partnerübergreifender Anwendungen(2022) Wild, Karoline; Leymann, Frank (Prof. Dr. Dr. h. c.)Ein wesentlicher Aspekt einer effektiven Kollaboration innerhalb von Organisationen, aber vor allem organisationsübergreifend, ist die Integration und Automatisierung der Prozesse. Dazu zählt auch die Bereitstellung von Anwendungssystemen, deren Komponenten von unterschiedlichen Partnern, das heißt Abteilungen oder Unternehmen, bereitgestellt und verwaltet werden. Die dadurch entstehende verteilte, dezentral verwaltete Umgebung bedarf neuer Konzepte zur Bereitstellung. Die Autonomie der Partner und die Verteilung der Komponenten führen dabei zu neuen Herausforderungen. Zum einen müssen partnerübergreifende Kommunikationsbeziehungen realisiert und zum anderen muss das automatisierte dezentrale Deployment ermöglicht werden. Eine Vielzahl von Technologien wurde in den letzten Jahren entwickelt, die alle Schritte von der Modellierung bis zur Bereitstellung und dem Management zur Laufzeit einer Anwendung abdecken. Diese Technologien basieren jedoch auf einer zentralisierten Koordination des Deployments, wodurch die Autonomie der Partner eingeschränkt ist. Auch fehlen Konzepte zur Identifikation von Problemen, die aus der Verteilung von Anwendungskomponenten resultieren und die Funktionsfähigkeit der Anwendung einschränken. Dies betrifft speziell die partnerübergreifenden Kommunikationsbeziehungen. Um diese Herausforderungen zu lösen, stellt diese Arbeit die DivA-Methode zum Verteilen, Adaptieren und Deployment partnerübergreifender Anwendungen vor. Die Methode vereinigt die globalen und lokalen Partneraktivitäten, die zur Bereitstellung partnerübergreifender Anwendungen benötigt werden. Dabei setzt die Methode auf dem deklarativen Essential Deployment Meta Model (EDMM) auf und ermöglicht damit die Einführung deploymenttechnologieunabhängiger Modellierungskonzepte zur Verteilung von Anwendungskomponenten sowie zur Modellanalyse und -adaption. Das Split-and-Match-Verfahren wird für die Verteilung von Anwendungskomponenten basierend auf festgelegten Zielumgebungen und zur Selektion kompatibler Cloud-Dienste vorgestellt. Für die Ausführung des Deployments können EDMM-Modelle in unterschiedliche Technologien transformiert werden. Um die Bereitstellung komplett dezentral durchzuführen, werden deklarative und imperative Technologien kombiniert und basierend auf den deklarativen EDMM-Modellen Workflows generiert, die die Aktivitäten zur Bereitstellung und zum Datenaustausch mit anderen Partnern zur Realisierung partnerübergreifender Kommunikationsbeziehungen orchestrieren. Diese Workflows formen implizit eine Deployment-Choreographie. Für die Modellanalyse und -adaption wird als Kern dieser Arbeit ein zweistufiges musterbasiertes Verfahren zur Problemerkennung und Modelladaption eingeführt. Dafür werden aus den textuellen Musterbeschreibungen die Problem- und Kontextdefinition analysiert und formalisiert, um die automatisierte Identifikation von Problemen in EDMM-Modellen zu ermöglichen. Besonderer Fokus liegt dabei auf Problemen, die durch die Verteilung der Komponenten entstehen und die Realisierung von Kommunikationsbeziehungen verhindern. Das gleiche Verfahren wird auch für die Selektion geeigneter konkreter Lösungsimplementierungen zur Behebung der Probleme angewendet. Zusätzlich wird ein Ansatz zur Selektion von Kommunikationstreibern abhängig von der verwendeten Integrations-Middleware vorgestellt, wodurch die Portabilität von Anwendungskomponenten verbessert werden kann. Die in dieser Arbeit vorgestellten Konzepte werden durch das DivA-Werkzeug automatisiert. Zur Validierung wird das Werkzeug prototypisch implementiert und in bestehende Systeme zur Modellierung und Ausführung des Deployments von Anwendungssystemen integriert.Item Open Access Automated composition of adaptive pervasive applications in heterogeneous environments(2012) Schuhmann, Stephan Andreas; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Distributed applications for Pervasive Computing represent a research area of high interest. Configuration processes are needed before the application execution to find a composition of components that provides the required functionality. As dynamic pervasive environments and device failures may yield unavailability of arbitrary components and devices at any time, finding and maintaining such a composition represents a nontrivial task. Obviously, many degrees of decentralization and even completely centralized approaches are possible in the calculation of valid configurations, spanning a wide spectrum of possible solutions. As configuration processes produce latencies which are noticed by the application user as undesired waiting times, configurations have to be calculated as fast as possible. While completely distributed configuration is inevitable in infrastructure-less Ad Hoc scenarios, many realistic Pervasive Computing environments are located in heterogeneous environments, where additional computation power of resource-rich devices can be utilized by centralized approaches. However, in case of strongly heterogeneous pervasive environments including several resource-rich and resource-weak devices, both centralized and decentralized approaches may lead to suboptimal results concerning configuration latencies: While the resource-weak devices may be bottlenecks for decentralized configuration, the centralized approach faces the problem of not utilizing parallelism. Most of the conducted projects in Pervasive Computing only focus on one specific type of environment: Either they concentrate on heterogeneous environments, which rely on additional infrastructure devices, leading to inapplicability in infrastructure-less environments. Or they address homogeneous Ad Hoc environments and treat all involved devices as equal, which leads to suboptimal results in case of present resource-rich devices, as their additional computation power is not exploited. Therefore, in this work we propose an advanced comprehensive adaptive approach that particularly focuses on the efficient support of heterogeneous environments, but is also applicable in infrastructure-less homogeneous scenarios. We provide multiple configuration schemes with different degrees of decentralization for distributed applications, optimized for specific scenarios. Our solution is adaptive in a way that the actual scheme is chosen based on the current system environment and calculates application compositions in a resource-aware efficient manner. This ensures high efficiency even in dynamically changing environments. Beyond this, many typical pervasive environments contain a fixed set of applications and devices that are frequently used. In such scenarios, identical resources are part of subsequent configuration calculations. Thus, the involved devices undergo a quite similar configuration process whenever an application is launched. However, starting the configuration from scratch every time not only consumes a lot of time, but also increases communication overhead and energy consumption of the involved devices. Therefore, our solution integrates the results from previous configurations to reduce the severity of the configuration problem in dynamic scenarios. We prove in prototypical real-world evaluations as well as by simulation and emulation that our comprehensive approach provides efficient automated configuration in the complete spectrum of possible application scenarios. This extensive functionality has not been achieved by related projects yet. Thus, our work supplies a significant contribution towards seamless application configuration in Pervasive Computing.Item Open Access Elastic parallel systems for high performance cloud computing(2020) Kehrer, Stefan; Blochinger, Wolfgang (Prof. Dr.)High Performance Computing (HPC) enables significant progress in both science and industry. Whereas traditionally parallel applications have been developed to address the grand challenges in science, as of today, they are also heavily used to speed up the time-to-result in the context of product design, production planning, financial risk management, medical diagnosis, as well as research and development efforts. However, purchasing and operating HPC clusters to run these applications requires huge capital expenditures as well as operational knowledge and thus is reserved to large organizations that benefit from economies of scale. More recently, the cloud evolved into an alternative execution environment for parallel applications, which comes with novel characteristics such as on-demand access to compute resources, pay-per-use, and elasticity. Whereas the cloud has been mainly used to operate interactive multi-tier applications, HPC users are also interested in the benefits offered. These include full control of the resource configuration based on virtualization, fast setup times by using on-demand accessible compute resources, and eliminated upfront capital expenditures due to the pay-per-use billing model. Additionally, elasticity allows compute resources to be provisioned and decommissioned at runtime, which allows fine-grained control of an application's performance in terms of its execution time and efficiency as well as the related monetary costs of the computation. Whereas HPC-optimized cloud environments have been introduced by cloud providers such as Amazon Web Services (AWS) and Microsoft Azure, existing parallel architectures are not designed to make use of elasticity. This thesis addresses several challenges in the emergent field of High Performance Cloud Computing. In particular, the presented contributions focus on the novel opportunities and challenges related to elasticity. First, the principles of elastic parallel systems as well as related design considerations are discussed in detail. On this basis, two exemplary elastic parallel system architectures are presented, each of which includes (1) an elasticity controller that controls the number of processing units based on user-defined goals, (2) a cloud-aware parallel execution model that handles coordination and synchronization requirements in an automated manner, and (3) a programming abstraction to ease the implementation of elastic parallel applications. To automate application delivery and deployment, novel approaches are presented that generate the required deployment artifacts from developer-provided source code in an automated manner while considering application-specific non-functional requirements. Throughout this thesis, a broad spectrum of design decisions related to the construction of elastic parallel system architectures is discussed, including proactive and reactive elasticity control mechanisms as well as cloud-based parallel processing with virtual machines (Infrastructure as a Service) and functions (Function as a Service). To evaluate these contributions, extensive experimental evaluations are presented.Item Open Access Models for data-efficient reinforcement learning on real-world applications(2021) Dörr, Andreas; Toussaint, Marc (Prof. Dr.)Large-scale deep Reinforcement Learning is strongly contributing to many recently published success stories of Artificial Intelligence. These techniques enabled computer systems to autonomously learn and master challenging problems, such as playing the game of Go or complex strategy games such as Star-Craft on human levels or above. Naturally, the question arises which problems could be addressed with these Reinforcement Learning technologies in industrial applications. So far, machine learning technologies based on (semi-)supervised learning create the most visible impact in industrial applications. For example, image, video or text understanding are primarily dominated by models trained and derived autonomously from large-scale data sets with modern (deep) machine learning methods. Reinforcement Learning, on the opposite side, however, deals with temporal decision-making problems and is much less commonly found in the industrial context. In these problems, current decisions and actions inevitably influence the outcome and success of a process much further down the road. This work strives to address some of the core problems, which prevent the effective use of Reinforcement Learning in industrial settings. Autonomous learning of new skills is always guided by existing priors that allow for generalization from previous experience. In some scenarios, non-existing or uninformative prior knowledge can be mitigated by vast amounts of experience for a particular task at hand. Typical industrial processes are, however, operated in very restricted, tightly calibrated operating points. Exploring the space of possible actions or changes to the process naively on the search for improved performance tends to be costly or even prohibitively dangerous. Therefore, one reoccurring subject throughout this work is the emergence of priors and model structures that allow for efficient use of all available experience data. A promising direction is Model-Based Reinforcement Learning, which is explored in the first part of this work. This part derives an automatic tuning method for one of themostcommonindustrial control architectures, the PID controller. By leveraging all available data about the system’s behavior in learning a system dynamics model, the derived method can efficiently tune these controllers from scratch. Although we can easily incorporate all data into dynamics models, real systems expose additional problems to the dynamics modeling and learning task. Characteristics such as non-Gaussian noise, latent states, feedback control or non-i.i.d. data regularly prevent using off-the-shelf modeling tools. Therefore, the second part of this work is concerned with the derivation of modeling solutions that are particularly suited for the reinforcement learning problem. Despite the predominant focus on model-based reinforcement learning as a promising, data-efficient learning tool, this work’s final part revisits model assumptions in a separate branch of reinforcement learning algorithms. Again, generalization and, therefore, efficient learning in model-based methods is primarily driven by the incorporated model assumptions (e.g., smooth dynamics), which real, discontinuous processes might heavily violate. To this end, a model-free reinforcement learning is presented that carefully reintroduces prior model structure to facilitate efficient learning without the need for strong dynamic model priors. The methods and solutions proposed in this work are grounded in the challenges experienced when operating with real-world hardware systems. With applications on a humanoid upper-body robot or an autonomous model race car, the proposed methods are demonstrated to successfully model and master their complex behavior.