Universitätsbibliothek

Software performance is of particular relevance to software system design, operation, and evolution because it has a significant impact on key business indicators. During the life-cycle of a software system, its implementation, configuration, and deployment are subject to multiple changes that may affect the end-to-end performance characteristics. Consequently, performance analysts continually need to provide answers to and act based on performance-relevant concerns. To ensure a desired level of performance, software performance engineering provides a plethora of methods, techniques, and tools for measuring, modeling, and evaluating performance properties of software systems. However, the answering of performance concerns is subject to a significant semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted. Performance evaluation approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. For the involved stakeholders, it can be an elaborate process to reasonably select, parameterize and correctly apply performance evaluation approaches, and to filter and interpret the obtained results. An additional challenge is that available performance evaluation artifacts may change over time, which requires to switch between different measurement-based and model-based performance evaluation approaches during the system evolution. At model-based analysis, the effort involved in creating performance models can also outweigh their benefits. To overcome the deficiencies and enable an automatic and holistic evaluation of performance throughout the software engineering life-cycle requires an approach that: (i) integrates multiple types of performance concerns and evaluation approaches, (ii) automates performance model creation, and (iii) automatically selects an evaluation methodology tailored to a specific scenario. This thesis presents a declarative approach —called Declarative Performance Engineering (DPE)— to automate performance evaluation based on a humanreadable specification of performance-related concerns. To this end, we separate the definition of performance concerns from their solution. The primary scientific contributions presented in this thesis are: A declarative language to express performance-related concerns and a corresponding processing framework: We provide a language to specify performance concerns independent of a concrete performance evaluation approach. Besides the specification of functional aspects, the language allows to include non-functional tradeoffs optionally. To answer these concerns, we provide a framework architecture and a corresponding reference implementation to process performance concerns automatically. It allows to integrate arbitrary performance evaluation approaches and is accompanied by reference implementations for model-based and measurement-based performance evaluation. Automated creation of architectural performance models from execution traces: The creation of performance models can be subject to significant efforts outweighing the benefits of model-based performance evaluation. We provide a model extraction framework that creates architectural performance models based on execution traces, provided by monitoring tools.The framework separates the derivation of generic information from model creation routines. To derive generic information, the framework combines state-of-the-art extraction and estimation techniques. We isolate object creation routines specified in a generic model builder interface based on concepts present in multiple performance-annotated architectural modeling formalisms. To create model extraction for a novel performance modeling formalism, developers only need to write object creation routines instead of creating model extraction software from scratch when reusing the generic framework. Automated and extensible decision support for performance evaluation approaches: We present a methodology and tooling for the automated selection of a performance evaluation approach tailored to the user concerns and application scenario. To this end, we propose to decouple the complexity of selecting a performance evaluation approach for a given scenario by providing solution approach capability models and a generic decision engine. The proposed capability meta-model enables to describe functional and non-functional capabilities of performance evaluation approaches and tools at different granularities. In contrast to existing tree-based decision support mechanisms, the decoupling approach allows to easily update characteristics of solution approaches as well as appending new rating criteria and thereby stay abreast of evolution in performance evaluation tooling and system technologies. Time-to-result estimation for model-based performance prediction: The time required to execute a model-based analysis plays an important role in different decision processes. For example, evaluation scenarios might require the prediction results to be available in a limited period of time such that the system can be adapted in time to ensure the desired quality of service. We propose a method to estimate the time-to-result for modelbased performance prediction based on model characteristics and analysis parametrization. We learn a prediction model using performancerelevant features thatwe determined using statistical tests. We implement the approach and demonstrate its practicability by applying it to analyze a simulation-based multi-step performance evaluation approach for a representative architectural performance modeling formalism. We validate each of the contributions based on representative case studies. The evaluation of automatic performance model extraction for two case study systems shows that the resulting models can accurately predict the performance behavior. Prediction accuracy errors are below 3% for resource utilization and mostly less than 20% for service response time. The separate evaluation of the reusability shows that the presented approach lowers the implementation efforts for automated model extraction tools by up to 91%. Based on two case studies applying measurement-based and model-based performance evaluation techniques, we demonstrate the suitability of the declarative performance engineering framework to answer multiple kinds of performance concerns customized to non-functional goals. Subsequently, we discuss reduced efforts in applying performance analyses using the integrated and automated declarative approach. Also, the evaluation of the declarative framework reviews benefits and savings integrating performance evaluation approaches into the declarative performance engineering framework. We demonstrate the applicability of the decision framework for performance evaluation approaches by applying it to depict existing decision trees. Then, we show how we can quickly adapt to the evolution of performance evaluation methods which is challenging for static tree-based decision support systems. At this, we show how to cope with the evolution of functional and non-functional capabilities of performance evaluation software and explain how to integrate new approaches. Finally, we evaluate the accuracy of the time-to-result estimation for a set of machinelearning algorithms and different training datasets. The predictions exhibit a mean percentage error below 20%, which can be further improved by including performance evaluations of the considered model into the training data. The presented contributions represent a significant step towards an integrated performance engineering process that combines the strengths of model-based and measurement-based performance evaluation. The proposed performance concern language in conjunction with the processing framework significantly reduces the complexity of applying performance evaluations for all stakeholders. Thereby it enables performance awareness throughout the software engineering life-cycle. The proposed performance concern language removes the semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted by the user.

The present dissertation investigates the management of RFID implementations in retail trade. Our work contributes to this by investigating important aspects that have so far received little attention in scientific literature. We therefore perform three studies about three important aspects of managing RFID implementations. We evaluate in our first study customer acceptance of pervasive retail systems using privacy calculus theory. The results of our study reveal the most important aspects a retailer has to consider when implementing pervasive retail systems. In our second study we analyze RFID-enabled robotic inventory taking with the help of a simulation model. The results show that retailers should implement robotic inventory taking if the accuracy rates of the robots are as high as the robots’ manufacturers claim. In our third and last study we evaluate the potentials of RFID data for supporting managerial decision making. We propose three novel methods in order to extract useful information from RFID data and propose a generic information extraction process. Our work is geared towards practitioners who want to improve their RFID-enabled processes and towards scientists conducting RFID-based research.

The field of human-computer interaction (HCI) strives for innovative user interfaces. Innovative and novel user interfaces are a challenge for a growing population of older users and endanger older adults to be excluded from an increasingly digital world. This is because older adults often have lower cognitive abilities and little prior experiences with technology. This thesis aims at resolving the tension between innovation and age-inclusiveness by developing user interfaces that can be used regardless of cognitive abilities and technology-dependent prior knowledge. The method of image-schematic metaphors holds promises for innovative and age-inclusive interaction design. Image-schematic metaphors represent a form of technology-independent prior knowledge. They reveal basic mental models and can be gathered in language (e.g. bank account is container from "I put money into my bank account"). Based on a discussion of previous applications of image-schematic metaphors in HCI, the present work derives three empirical research questions regarding image-schematic metaphors for innovative and age-inclusive interaction design. The first research question addresses the yet untested assumption that younger and older adults overlap in their technology-independent prior knowledge and, therefore, their usage of image-schematic metaphors. In study 1, a total of 41 participants described abstract concepts from the domains of online banking and everyday life. In study 2, ten contextual interviews were conducted. In both studies, younger and older adults showed a substantial overlap of 70% to 75%, indicating that also their mental models overlap substantially. The second research question addresses the applicability and potential of image-schematic metaphors for innovative design from the perspective of designers. In study 3, 18 student design teams completed an ideation process with either an affinity diagram as the industry standard, image-schematic metaphors or both methods in combination and created paper prototypes. The image-schematic metaphor method alone, but not the combination of both methods, was readily adopted and applied just as a well as the more familiar standard method. In study 4, professional interaction designers created prototypes either with or without image-schematic metaphors. In both studies, the method of image-schematic metaphors was perceived as applicable and creativity stimulating. The third research question addresses whether designs that explicitly follow image-schematic metaphors are more innovative and age-inclusive regarding differences in cognitive abilities and prior technological knowledge. In two experimental studies (study 5 and 6) involving a total of 54 younger and 53 older adults, prototypes that were designed with image-schematic metaphors were perceived as more innovative compared to those who were designed without image-schematic metaphors. Moreover, the impact of prior technological knowledge on interaction was reduced for prototypes that had been designed with image-schematic metaphors. However, participants' cognitive abilities and age still influenced the interaction significantly. The present work provides empirical as well as methodological findings that can help to promote the method of image-schematic metaphors in interaction design. As a result of these studies it can be concluded that the image-schematic metaphors are an applicable and effective method for innovative user interfaces that can be used regardless of prior technological knowledge.

Telemedicine uses telecommunication and information technology to provide health care services over spatial distances. In the upcoming demographic changes towards an older average population age, especially rural areas suffer from a decreasing doctor to patient ratio as well as a limited amount of available medical specialists in acceptable distance. These areas could benefit the most from telemedicine applications as they are known to improve access to medical services, medical expertise and can also help to mitigate critical or emergency situations. Although the possibilities of telemedicine applications exist in the entire range of healthcare, current systems focus on one specific disease while using dedicated hardware to connect the patient with the supervising telemedicine center. This thesis describes the development of a telemedical system which follows a new generic design approach. This bridges the gap of existing approaches that only tackle one specific application. The proposed system on the contrary aims at supporting as many diseases and use cases as possible by taking all the stakeholders into account at the same time. To address the usability and acceptance of the system it is designed to use standardized hardware like commercial medical sensors and smartphones for collecting medical data of the patients and transmitting them to the telemedical center. The smartphone can also act as interface to the patient for health questionnaires or feedback. The system can handle the collection and transport of medical data, analysis and visualization of the data as well as providing a real time communication with video and audio between the users. On top of the generic telemedical framework the issue of scalability is addressed by integrating a rule-based analysis tool for the medical data. Rules can be easily created by medical personnel via a visual editor and can be personalized for each patient. The rule-based analysis tool is extended by multiple options for visualization of the data, mechanisms to handle complex rules and options for performing actions like raising alarms or sending automated messages. It is sometimes hard for the medical experts to formulate their knowledge into rules and there may be information in the medical data that is not yet known. This is why a machine learning module was integrated into the system. It uses the incoming medical data of the patients to learn new rules that are then presented to the medical personnel for inspection. This is in line with European legislation where the human still needs to be in charge of such decisions. Overall, we were able to show the benefit of the generic approach by evaluating it in three completely different medical use cases derived from specific application needs: monitoring of COPD (chronic obstructive pulmonary disease) patients, support of patients performing dialysis at home and councils of intensive-care experts. In addition the system was used for a non-medical use case: monitoring and optimization of industrial machines and robots. In all of the mentioned cases, we were able to prove the robustness of the generic approach with real users of the corresponding domain. This is why we can propose this approach for future development of telemedical systems.

The success of semantic systems has been proven over the last years. Nowadays, Linked Data is the driver for the rapid development of ever new intelligent systems. Especially in enterprise environments semantic systems successfully support more and more business processes. This is especially true for after sales service in the mechanical engineering domain. Here, service technicians need effective access to relevant technical documentation in order to diagnose and solve problems and defects. Therefore, the usage of semantic information retrieval systems has become the new system metaphor. Unlike classical retrieval software Linked Enterprise Data graphs are exploited to grant targeted and problem-oriented access to relevant documents. However, huge parts of legacy technical documents have not yet been integrated into Linked Enterprise Data graphs. Additionally, a plethora of information models for the semantic representation of technical information exists. The semantic maturity of these information models can hardly be measured. This thesis motivates that there is an inherent need for a self-contained semantification approach for technical documents. This work introduces a maturity model that allows to quickly assess existing documentation. Additionally, the approach comprises an abstracting semantic representation for technical documents that is aligned to all major standard information models. The semantic representation combines structural and rhetorical aspects to provide access to so called Core Documentation Entities. A novel and holistic semantification process describes how technical documents in different legacy formats can be transformed to a semantic and linked representation. The practical significance of the semantification approach depends on tools supporting its application. This work presents an accompanying tool chain of semantification applications, especially the semantification framework CAPLAN that is a highly integrated development and runtime environment for semantification processes. The complete semantification approach is evaluated in four real-life projects: in a spare part augmentation project, semantification projects for earth moving technology and harvesting technology, as well as an ontology population project for special purpose vehicles. Three additional case studies underline the broad applicability of the presented ideas.