Large-scale social-media analytics on stratosphere

The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows

Discursive design thinking: the role of explicit knowledge in creative architectural design reasoning

The main hypothesis investigated in this paper is based upon the suggestion that the discursive reasoning in architecture supported by an explicit knowledge of spatial configurations can enhance both design productivity and the intelligibility of design solutions. The study consists of an examination of an architect’s performance while solving intuitively a well-defined problem followed by an analysis of the spatial structure of their design solutions. One group of architects will attempt to solve the design problem logically, rationalizing their design decisions by implementing their explicit knowledge of spatial configurations. The other group will use an implicit form of such knowledge arising from their architectural education to reason about their design acts. An integrated model of protocol analysis combining linkography and macroscopic coding is used to analyze the design processes. The resulting design outcomes will be evaluated quantitatively in terms of their spatial configurations. The analysis appears to show that an explicit knowledge of the rules of spatial configurations, as possessed by the first group of architects can partially enhance their function-driven judgment producing permeable and well-structured spaces. These findings are particularly significant as they imply that an explicit rather than an implicit knowledge of the fundamental rules that make a layout possible can lead to a considerable improvement in both the design process and product. This suggests that by externalizing th

The role of dobutamine stress cardiovascular magnetic resonance in the clinical management of patients with suspected and known coronary artery disease

BACKGROUND: Recent studies have demonstrated the consistently high diagnostic and prognostic value of dobutamine stress cardiovascular magnetic resonance (DCMR). The value of DCMR for clinical decision making still needs to be defined. Hence, the purpose of this study was to assess the utility of DCMR regarding clinical management of patients with suspected and known coronary artery disease (CAD) in a routine setting. METHODS AND RESULTS: We prospectively performed a standard DCMR examination in 1532 consecutive patients with suspected and known CAD. Patients were stratified according to the results of DCMR: DCMR-positive patients were recommended to undergo invasive coronary angiography and DCMR-negative patients received optimal medical treatment. Of 609 (40%) DCMR-positive patients coronary angiography was performed in 478 (78%) within 90 days. In 409 of these patients significant coronary stenoses ≥ 50% were present (positive predictive value 86%). Of 923 (60%) DCMR-negative patients 833 (90%) received optimal medical therapy. During a mean follow-up period of 2.1 ± 0.8 years (median: 2.1 years, interquartile range 1.5 to 2.7 years) 8 DCMR-negative patients (0.96%) sustained a cardiac event.In 131 DCMR-positive patients who did not undergo invasive angiography, 20 patients (15%) suffered cardiac events. In 90 DCMR-negative patients (10%) invasive angiography was performed within 2 years (range 0.01 to 2.0 years) with 56 patients having coronary stenoses ≥ 50%. CONCLUSION: In a routine setting DCMR proved a useful arbiter for clinical decision making and exhibited high utility for stratification and clinical management of patients with suspected and known CAD

Insights into the Molecular Evolution of the PDZ/LIM Family and Identification of a Novel Conserved Protein Motif

The PDZ and LIM domain-containing protein family is encoded by a diverse group of genes whose phylogeny has currently not been analyzed. In mammals, ten genes are found that encode both a PDZ- and one or several LIM-domains. These genes are: ALP, RIL, Elfin (CLP36), Mystique, Enigma (LMP-1), Enigma homologue (ENH), ZASP (Cypher, Oracle), LMO7 and the two LIM domain kinases (LIMK1 and LIMK2). As conventional alignment and phylogenetic procedures of full-length sequences fell short of elucidating the evolutionary history of these genes, we started to analyze the PDZ and LIM domain sequences themselves. Using information from most sequenced eukaryotic lineages, our phylogenetic analysis is based on full-length cDNA-, EST-derived- and genomic- PDZ and LIM domain sequences of over 25 species, ranging from yeast to humans. Plant and protozoan homologs were not found. Our phylogenetic analysis identifies a number of domain duplication and rearrangement events, and shows a single convergent event during evolution of the PDZ/LIM family. Further, we describe the separation of the ALP and Enigma subfamilies in lower vertebrates and identify a novel consensus motif, which we call ‘ALP-like motif’ (AM). This motif is highly-conserved between ALP subfamily proteins of diverse organisms. We used here a combinatorial approach to define the relation of the PDZ and LIM domain encoding genes and to reconstruct their phylogeny. This analysis allowed us to classify the PDZ/LIM family and to suggest a meaningful model for the molecular evolution of the diverse gene architectures found in this multi-domain family

Human Practice. Digital Ecologies. Our Future. : 14. Internationale Tagung Wirtschaftsinformatik (WI 2019) : Tagungsband

Erschienen bei: universi - Universitätsverlag Siegen. - ISBN: 978-3-96182-063-4Aus dem Inhalt: Track 1: Produktion & Cyber-Physische Systeme Requirements and a Meta Model for Exchanging Additive Manufacturing Capacities Service Systems, Smart Service Systems and Cyber- Physical Systems—What’s the difference? Towards a Unified Terminology Developing an Industrial IoT Platform – Trade-off between Horizontal and Vertical Approaches Machine Learning und Complex Event Processing: Effiziente Echtzeitauswertung am Beispiel Smart Factory Sensor retrofit for a coffee machine as condition monitoring and predictive maintenance use case Stakeholder-Analyse zum Einsatz IIoT-basierter Frischeinformationen in der Lebensmittelindustrie Towards a Framework for Predictive Maintenance Strategies in Mechanical Engineering - A Method-Oriented Literature Analysis Development of a matching platform for the requirement-oriented selection of cyber physical systems for SMEs Track 2: Logistic Analytics An Empirical Study of Customers’ Behavioral Intention to Use Ridepooling Services – An Extension of the Technology Acceptance Model Modeling Delay Propagation and Transmission in Railway Networks What is the impact of company specific adjustments on the acceptance and diffusion of logistic standards? Robust Route Planning in Intermodal Urban Traffic Track 3: Unternehmensmodellierung & Informationssystemgestaltung (Enterprise Modelling & Information Systems Design) Work System Modeling Method with Different Levels of Specificity and Rigor for Different Stakeholder Purposes Resolving Inconsistencies in Declarative Process Models based on Culpability Measurement Strategic Analysis in the Realm of Enterprise Modeling – On the Example of Blockchain-Based Initiatives for the Electricity Sector Zwischenbetriebliche Integration in der Möbelbranche: Konfigurationen und Einflussfaktoren Novices’ Quality Perceptions and the Acceptance of Process Modeling Grammars Entwicklung einer Definition für Social Business Objects (SBO) zur Modellierung von Unternehmensinformationen Designing a Reference Model for Digital Product Configurators Terminology for Evolving Design Artifacts Business Role-Object Specification: A Language for Behavior-aware Structural Modeling of Business Objects Generating Smart Glasses-based Information Systems with BPMN4SGA: A BPMN Extension for Smart Glasses Applications Using Blockchain in Peer-to-Peer Carsharing to Build Trust in the Sharing Economy Testing in Big Data: An Architecture Pattern for a Development Environment for Innovative, Integrated and Robust Applications Track 4: Lern- und Wissensmanagement (e-Learning and Knowledge Management) eGovernment Competences revisited – A Literature Review on necessary Competences in a Digitalized Public Sector Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents Teaching the Digital Transformation of Business Processes: Design of a Simulation Game for Information Systems Education Conceptualizing Immersion for Individual Learning in Virtual Reality Designing a Flipped Classroom Course – a Process Model The Influence of Risk-Taking on Knowledge Exchange and Combination Gamified Feedback durch Avatare im Mobile Learning Alexa, Can You Help Me Solve That Problem? - Understanding the Value of Smart Personal Assistants as Tutors for Complex Problem Tasks Track 5: Data Science & Business Analytics Matching with Bundle Preferences: Tradeoff between Fairness and Truthfulness Applied image recognition: guidelines for using deep learning models in practice Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting Reading Between the Lines of Qualitative Data – How to Detect Hidden Structure Based on Codes Online Auctions with Dual-Threshold Algorithms: An Experimental Study and Practical Evaluation Design Features of Non-Financial Reward Programs for Online Reviews: Evaluation based on Google Maps Data Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics Leveraging Unstructured Image Data for Product Quality Improvement Decision Support for Real Estate Investors: Improving Real Estate Valuation with 3D City Models and Points of Interest Knowledge Discovery from CVs: A Topic Modeling Procedure Online Product Descriptions – Boost for your Sales? Entscheidungsunterstützung durch historienbasierte Dienstreihenfolgeplanung mit Pattern A Semi-Automated Approach for Generating Online Review Templates Machine Learning goes Measure Management: Leveraging Anomaly Detection and Parts Search to Improve Product-Cost Optimization Bedeutung von Predictive Analytics für den theoretischen Erkenntnisgewinn in der IS-Forschung Track 6: Digitale Transformation und Dienstleistungen Heuristic Theorizing in Software Development: Deriving Design Principles for Smart Glasses-based Systems Mirroring E-service for Brick and Mortar Retail: An Assessment and Survey Taxonomy of Digital Platforms: A Platform Architecture Perspective Value of Star Players in the Digital Age Local Shopping Platforms – Harnessing Locational Advantages for the Digital Transformation of Local Retail Outlets: A Content Analysis A Socio-Technical Approach to Manage Analytics-as-a-Service – Results of an Action Design Research Project Characterizing Approaches to Digital Transformation: Development of a Taxonomy of Digital Units Expectations vs. Reality – Benefits of Smart Services in the Field of Tension between Industry and Science Innovation Networks and Digital Innovation: How Organizations Use Innovation Networks in a Digitized Environment Characterising Social Reading Platforms— A Taxonomy-Based Approach to Structure the Field Less Complex than Expected – What Really Drives IT Consulting Value Modularity Canvas – A Framework for Visualizing Potentials of Service Modularity Towards a Conceptualization of Capabilities for Innovating Business Models in the Industrial Internet of Things A Taxonomy of Barriers to Digital Transformation Ambidexterity in Service Innovation Research: A Systematic Literature Review Design and success factors of an online solution for cross-pillar pension information Track 7: IT-Management und -Strategie A Frugal Support Structure for New Software Implementations in SMEs How to Structure a Company-wide Adoption of Big Data Analytics The Changing Roles of Innovation Actors and Organizational Antecedents in the Digital Age Bewertung des Kundennutzens von Chatbots für den Einsatz im Servicedesk Understanding the Benefits of Agile Software Development in Regulated Environments Are Employees Following the Rules? On the Effectiveness of IT Consumerization Policies Agile and Attached: The Impact of Agile Practices on Agile Team Members’ Affective Organisational Commitment The Complexity Trap – Limits of IT Flexibility for Supporting Organizational Agility in Decentralized Organizations Platform Openness: A Systematic Literature Review and Avenues for Future Research Competence, Fashion and the Case of Blockchain The Digital Platform Otto.de: A Case Study of Growth, Complexity, and Generativity Track 8: eHealth & alternde Gesellschaft Security and Privacy of Personal Health Records in Cloud Computing Environments – An Experimental Exploration of the Impact of Storage Solutions and Data Breaches Patientenintegration durch Pfadsysteme Digitalisierung in der Stressprävention – eine qualitative Interviewstudie zu Nutzenpotenzialen User Dynamics in Mental Health Forums – A Sentiment Analysis Perspective Intent and the Use of Wearables in the Workplace – A Model Development Understanding Patient Pathways in the Context of Integrated Health Care Services - Implications from a Scoping Review Understanding the Habitual Use of Wearable Activity Trackers On the Fit in Fitness Apps: Studying the Interaction of Motivational Affordances and Users’ Goal Orientations in Affecting the Benefits Gained Gamification in Health Behavior Change Support Systems - A Synthesis of Unintended Side Effects Investigating the Influence of Information Incongruity on Trust-Relations within Trilateral Healthcare Settings Track 9: Krisen- und Kontinuitätsmanagement Potentiale von IKT beim Ausfall kritischer Infrastrukturen: Erwartungen, Informationsgewinnung und Mediennutzung der Zivilbevölkerung in Deutschland Fake News Perception in Germany: A Representative Study of People’s Attitudes and Approaches to Counteract Disinformation Analyzing the Potential of Graphical Building Information for Fire Emergency Responses: Findings from a Controlled Experiment Track 10: Human-Computer Interaction Towards a Taxonomy of Platforms for Conversational Agent Design Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis Self-Tracking and Gamification: Analyzing the Interplay of Motivations, Usage and Motivation Fulfillment Erfolgsfaktoren von Augmented-Reality-Applikationen: Analyse von Nutzerrezensionen mit dem Review-Mining-Verfahren Designing Dynamic Decision Support for Electronic Requirements Negotiations Who is Stressed by Using ICTs? A Qualitative Comparison Analysis with the Big Five Personality Traits to Understand Technostress Walking the Middle Path: How Medium Trade-Off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents Theory-Based Affordances of Utilitarian, Hedonic and Dual-Purposed Technologies: A Literature Review Eliciting Customer Preferences for Shopping Companion Apps: A Service Quality Approach The Role of Early User Participation in Discovering Software – A Case Study from the Context of Smart Glasses The Fluidity of the Self-Concept as a Framework to Explain the Motivation to Play Video Games Heart over Heels? An Empirical Analysis of the Relationship between Emotions and Review Helpfulness for Experience and Credence Goods Track 11: Information Security and Information Privacy Unfolding Concerns about Augmented Reality Technologies: A Qualitative Analysis of User Perceptions To (Psychologically) Own Data is to Protect Data: How Psychological Ownership Determines Protective Behavior in a Work and Private Context Understanding Data Protection Regulations from a Data Management Perspective: A Capability-Based Approach to EU-GDPR On the Difficulties of Incentivizing Online Privacy through Transparency: A Qualitative Survey of the German Health Insurance Market What is Your Selfie Worth? A Field Study on Individuals’ Valuation of Personal Data Justification of Mass Surveillance: A Quantitative Study An Exploratory Study of Risk Perception for Data Disclosure to a Network of Firms Track 12: Umweltinformatik und nachhaltiges Wirtschaften Kommunikationsfäden im Nadelöhr – Fachliche Prozessmodellierung der Nachhaltigkeitskommunikation am Kapitalmarkt Potentiale und Herausforderungen der Materialflusskostenrechnung Computing Incentives for User-Based Relocation in Carsharing Sustainability’s Coming Home: Preliminary Design Principles for the Sustainable Smart District Substitution of hazardous chemical substances using Deep Learning and t-SNE A Hierarchy of DSMLs in Support of Product Life-Cycle Assessment A Survey of Smart Energy Services for Private Households Door-to-Door Mobility Integrators as Keystone Organizations of Smart Ecosystems: Resources and Value Co-Creation – A Literature Review Ein Entscheidungsunterstützungssystem zur ökonomischen Bewertung von Mieterstrom auf Basis der Clusteranalyse Discovering Blockchain for Sustainable Product-Service Systems to enhance the Circular Economy Digitale Rückverfolgbarkeit von Lebensmitteln: Eine verbraucherinformatische Studie Umweltbewusstsein durch audiovisuelles Content Marketing? Eine experimentelle Untersuchung zur Konsumentenbewertung nachhaltiger Smartphones Towards Predictive Energy Management in Information Systems: A Research Proposal A Web Browser-Based Application for Processing and Analyzing Material Flow Models using the MFCA Methodology Track 13: Digital Work - Social, mobile, smart On Conversational Agents in Information Systems Research: Analyzing the Past to Guide Future Work The Potential of Augmented Reality for Improving Occupational First Aid Prevent a Vicious Circle! The Role of Organizational IT-Capability in Attracting IT-affine Applicants Good, Bad, or Both? Conceptualization and Measurement of Ambivalent User Attitudes Towards AI A Case Study on Cross-Hierarchical Communication in Digital Work Environments ‘Show Me Your People Skills’ - Employing CEO Branding for Corporate Reputation Management in Social Media A Multiorganisational Study of the Drivers and Barriers of Enterprise Collaboration Systems-Enabled Change The More the Merrier? The Effect of Size of Core Team Subgroups on Success of Open Source Projects The Impact of Anthropomorphic and Functional Chatbot Design Features in Enterprise Collaboration Systems on User Acceptance Digital Feedback for Digital Work? Affordances and Constraints of a Feedback App at InsurCorp The Effect of Marker-less Augmented Reality on Task and Learning Performance Antecedents for Cyberloafing – A Literature Review Internal Crowd Work as a Source of Empowerment - An Empirical Analysis of the Perception of Employees in a Crowdtesting Project Track 14: Geschäftsmodelle und digitales Unternehmertum Dividing the ICO Jungle: Extracting and Evaluating Design Archetypes Capturing Value from Data: Exploring Factors Influencing Revenue Model Design for Data-Driven Services Understanding the Role of Data for Innovating Business Models: A System Dynamics Perspective Business Model Innovation and Stakeholder: Exploring Mechanisms and Outcomes of Value Creation and Destruction Business Models for Internet of Things Platforms: Empirical Development of a Taxonomy and Archetypes Revitalizing established Industrial Companies: State of the Art and Success Principles of Digital Corporate Incubators When 1+1 is Greater than 2: Concurrence of Additional Digital and Established Business Models within Companies Special Track 1: Student Track Investigating Personalized Price Discrimination of Textile-, Electronics- and General Stores in German Online Retail From Facets to a Universal Definition – An Analysis of IoT Usage in Retail Is the Technostress Creators Inventory Still an Up-To-Date Measurement Instrument? Results of a Large-Scale Interview Study Application of Media Synchronicity Theory to Creative Tasks in Virtual Teams Using the Example of Design Thinking TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter Application of Process Mining Techniques to Support Maintenance-Related Objectives How Voice Can Change Customer Satisfaction: A Comparative Analysis between E-Commerce and Voice Commerce Business Process Compliance and Blockchain: How Does the Ethereum Blockchain Address Challenges of Business Process Compliance? Improving Business Model Configuration through a Question-based Approach The Influence of Situational Factors and Gamification on Intrinsic Motivation and Learning Evaluation von ITSM-Tools für Integration und Management von Cloud-Diensten am Beispiel von ServiceNow How Software Promotes the Integration of Sustainability in Business Process Management Criteria Catalog for Industrial IoT Platforms from the Perspective of the Machine Tool Industry Special Track 3: Demos & Prototyping Privacy-friendly User Location Tracking with Smart Devices: The BeaT Prototype Application-oriented robotics in nursing homes Augmented Reality for Set-up Processe Mixed Reality for supporting Remote-Meetings Gamification zur Motivationssteigerung von Werkern bei der Betriebsdatenerfassung Automatically Extracting and Analyzing Customer Needs from Twitter: A “Needmining” Prototype GaNEsHA: Opportunities for Sustainable Transportation in Smart Cities TUCANA: A platform for using local processing power of edge devices for building data-driven services Demonstrator zur Beschreibung und Visualisierung einer kritischen Infrastruktur Entwicklung einer alltagsnahen persuasiven App zur Bewegungsmotivation für ältere Nutzerinnen und Nutzer A browser-based modeling tool for studying the learning of conceptual modeling based on a multi-modal data collection approach Exergames & Dementia: An interactive System for People with Dementia and their Care-Network Workshops Workshop Ethics and Morality in Business Informatics (Workshop Ethik und Moral in der Wirtschaftsinformatik – EMoWI’19) Model-Based Compliance in Information Systems - Foundations, Case Description and Data Set of the MobIS-Challenge for Students and Doctoral Candidates Report of the Workshop on Concepts and Methods of Identifying Digital Potentials in Information Management Control of Systemic Risks in Global Networks - A Grand Challenge to Information Systems Research Die Mitarbeiter von morgen - Kompetenzen künftiger Mitarbeiter im Bereich Business Analytics Digitaler Konsum: Herausforderungen und Chancen der Verbraucherinformati

Benchmarking von Datenflusssystemen für skalierbares maschinelles Lernen

The popularity of the world wide web and its ubiquitous global online services have led to unprecedented amounts of available data. Novel distributed data processing systems have been developed in order to scale out computations and analysis to such massive data set sizes. These "Big Data Analytics" systems are also popular choices to scale out the execution of machine learning algorithms. However, it remains an open question how efficient they perform at this task and how to adequately evaluate and benchmark these systems for scalable machine learning workloads in general. In this thesis, we present work on all crucial building blocks for a benchmark of distributed data processing systems for scalable machine learning including extensive experimental evaluations of distributed data flow systems. First, we introduce a representative set of distributed machine learning algorithms suitable for large scale distributed settings which have close resemblance to industry-relevant applications and provide generalizable insights into system performance. We specify data sets, workloads, experiments and metrics that address all relevant aspects of scalability, including the important aspect of model dimensionality. We provide results of a comprehensive experimental evaluation of popular distributed dataflow systems, which highlight shortcomings in these systems. Our results show, that while being able to robustly scale with increasing data set sizes, current state of the art data flow systems are surprisingly inefficient at coping with high dimensional data, which is a crucial requirement for large scale machine learning algorithms. Second, we propose methods and experiments to explore the trade-off space between the runtime for training a machine learning model and the model quality. We make the case for state of the art, single machine algorithms as baselines when evaluating distributed data processing systems for scalable machine learning workloads and present such an experimental evaluation for two popular and representative machine learning algorithms with distributed data flow systems and single machine libraries. Our results show, that even latest generation distributed data flow systems require substantial hardware resources to provide comparable prediction quality to a state of the art single machine library within the same time frame. This insight is a valuable addition for future systems papers as well as for scientists and practitioners considering distributed data processing systems for applying machine learning algorithms to their problem domain. Third, we present work on reducing the operational complexity of carrying out benchmark experiments. We introduce a framework for defining, executing, analyzing and sharing experiments on distributed data processing systems. On the one hand, this framework automatically orchestrates experiments, on the other hand, it introduces a unified and transparent way of specifying experiments, including the actual application code, system configuration, and experiment setup description enabling the sharing of end-to-end experiment artifacts. With this, our framework fosters reproducibility and portability of benchmark experiments and significantly reduces the "entry barrier" to running benchmarks of distributed data processing systems.Die Popularität des World Wide Web und seiner allgegenwärtigen global verfügbaren Online-Dienste haben zu beispiellosen Mengen an verfügbaren Daten geführt. Im Lichte dieser Entwicklung wurden neuartige, verteilte Datenverarbeitungssysteme (sogenannte "Big Data Analytics''-Systeme) entwickelt, um Berechnungen und Analysen auf solch massive Datengrößen skalieren zu können. Diese Systeme sind ebenfalls beliebte Ausführungsumgebungen für das Skalieren von Algorithmen des maschinellen Lernens. Es ist jedoch eine offene Frage, wie effizient diese "Big Data Analytics''-Systeme bei der Ausführung von skalierbaren Verfahren des maschinellen Lernens sind und wie man solche Systeme adäquat evaluieren und benchmarken kann. In dieser Doktorarbeit stellen wir Arbeiten für alle essenziellen Bausteine einer solchen Evaluierung von verteilten Datenverarbeitungssystemen für skalierbare Methoden des Maschinellen Lernens, inklusive einer umfassenden experimentellen Evaluierung von verteilten Datenflusssystemen, vor. Zunächst stellen wir einen repräsentativen Satz verteilter maschineller Lernalgorithmen vor, welche für den Einsatz in massiv verteilten Umgebungen passend sind. Diese Lernalgorithmen besitzen substanzielle Ähnlichkeit zu einer breiten Palette von industrierelevanten Verfahren und bieten daher verallgemeinerbare Einblicke in die Systemleistung. Wir definieren Datensätze, Algorithmen, Experimente und Metriken, die alle relevanten Aspekte von Skalierbarkeit, einschließlich des wichtigen Aspekts der Modelldimensionalität abdecken. Wir präsentieren und diskutieren die Ergebnisse unserer umfassenden experimentellen Evaluierung gängiger verteilter Datenflusssysteme. Unsere Ergebnisse zeigen, dass die untersuchten aktuellen Datenflusssysteme zwar robust bzgl. der Anzahl der Rechner sowie der Datengröße skalieren können, jedoch bei der Skalierung der Modelldimensionalität substanzielle Schwächen aufweisen. Diese Ineffizienz überrascht, da die Bewältigung hochdimensionaler Daten eine Kernanforderung für das Ausführen skalierbarer maschineller Lernverfahren darstellt. Zweitens, schlagen wir Methoden und Experimente vor, um den zwischen Laufzeit des Trainings eines Modells des maschinellen Lernens und der Vorhersagequalität dieses Modells aufgespannten Raum zu erkunden. Wir argumentieren, dass effiziente, dem Stand der Technik entsprechende, Einzelmaschinenbibliotheken als Basis in vergleichenden Experimenten herangezogen werden sollen. Wir präsentieren Ergebnisse einer solchen Evaluierung für zwei populäre und repräsentative Algorithmen des maschinellen Lernens auf verteilten Datenflusssystemen und mit Einzelmaschinenbibliotheken. Die Ergebnisse unserer Experimente zeigen, dass selbst die neuesten verteilten Datenflusssysteme substanzielle Hardwareressourcen benötigen, um eine vergleichbare Vorhersagequalität zu Einzelmaschinenbibliotheken innerhalb vergleichbarer Trainingszeiträume zu erreichen. Dies ist eine wichtige Erkenntnis, welche für zukünftige Forschung und Entwicklung im Bereich der Datenverarbeitungssysteme zur Kenntnis genommen werden muss, aber auch eine relevante Information für Wissenschaftler und Anwender dieser Systeme, welche die Anwendung von verteilten Datenflusssystemen für Algorithmen des maschinellen Lernens in ihrer Domäne in Betracht ziehen. Drittens, präsentieren wir Arbeiten zur Reduzierung der operativen Komplexität bei der Durchführung von Benchmark-Experimenten. Wir stellen ein Framework für die Definition, Ausführung und Analyse von Experimenten auf verteilten Datenverarbeitungssystemen vor. Auf der einen Seite orchestriert unser Framework automatisch Experimente, auf der anderen Seite führt es eine einheitliche und transparente Art und Weise, Experimente zu spezifizieren, ein. Hierbei werden neben der eigentlichen Implementierung der Benchmarkalgorithmen auch sämtliche Parameter der Systemkonfiguration und die Beschreibung des Experimentaufbaus und der beteiligten Systeme und Komponenten inkludiert. Somit wird eine transparente Verfügbarmachung und das Teilen von kompletten "End-to-End'' Experimentartefakten ermöglicht. Hierdurch fördert unser Framework die Reproduzierbarkeit und Portabilität von Benchmark-Experimenten und reduziert die "Eintrittsbarriere" bzgl. der Durchführung von Benchmarks für verteilte Datenverarbeitungssysteme signifikant

Blade Deformation measurements with IPCT on an EC135 helicopter rotor

For the design of a helicopter rotor blade, it is crucial to predict the dynamic behaviour and occurring forces and moments. For the validation of prediction tools, reliable flight test data are required. Furthermore, flight test measurements are necessary for the calculation of fatigue loads. Today, strain gauges are applied to a helicopter rotor blade to perform measurements in flight test. Locally, they allow a precise strain measurement for the complete rotor revolution. The instrumentation implies a high effort. Furthermore, wiring can imply difficulties due to its weight and its perturbation of the aerodynamic shape. An optical measurement technique may overcome some of the limitations of strain gauges. A precise measurement of the deformation of the complete surface of the rotor blade allows to locate high strains and to identify oscillatory modes. The exact blade position can be identified optically. One of these advanced optical measurement techniques is the Image Pattern Correlation Technique (IPCT). Today, IPCT is a state of the art measurement technique for static and dynamic deformations. In AIM, DLR and Eurocopter explore the feasibility of Quantitative Video Technique (QVT) together with the Image Pattern Correlation Technique (IPCT) on the rotating main rotor blades of a flying helicopter

Blade Deformation Measurements with IPCT on an EC 135 Helicopter Rotor

For the design of a helicopter rotor blade, it is crucial to predict the dynamic behavior and occurring forces and moments. For the validation of prediction tools, reliable flight test data is required. Furthermore, flight test measurements are necessary for the calculation of fatigue loads. Today, strain gauges are applied to a helicopter rotor blade to perform measurements in flight test. Locally, they allow a precise strain measurement for the complete rotor revolution. This instrumentation implies a high effort. Furthermore, wiring can imply difficulties due to its weight and its modification of the aerodynamic shape. An optical measurement technique may overcome some of the limitations of strain gauges. A precise measurement of the deformation of the complete surface of the rotor blade allows to locate high strains and to identify oscillatory modes. The exact blade position can be identified optically. One of these advanced optical measurement techniques is the Image Pattern Correlation Technique (IPCT). Today, IPCT is a state of the art measurement technique for static and dynamic deformations. In AIM, DLR and Eurocopter explore the feasibility of Quantitative Video Technique (QVT) together with the Image Pattern Correlation Technique (IPCT) on the rotating main rotor blades of a flying helicopter. Ground tests of the measurement system on a whirl tower and a tied down helicopter are performed to verify the feasibility and performance of the measurement system previous to flight testing