A semantic sensor web for environmental decision support applications

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England

Naming and discovery in networks : architecture and economics

In less than three decades, the Internet was transformed from a research network available to the academic community into an international communication infrastructure. Despite its tremendous success, there is a growing consensus in the research community that the Internet has architectural limitations that need to be addressed in a effort to design a future Internet. Among the main technical limitations are the lack of mobility support, and the lack of security and trust. The Internet, and particularly TCP/IP, identifies endpoints using a location/routing identifier, the IP address. Coupling the endpoint identifier to the location identifier hinders mobility and poorly identifies the actual endpoint. On the other hand, the lack of security has been attributed to limitations in both the network and the endpoint. Authentication for example is one of the main concerns in the architecture and is hard to implement partly due to lack of identity support. The general problem that this dissertation is concerned with is that of designing a future Internet. Towards this end, we focus on two specific sub-problems. The first problem is the lack of a framework for thinking about architectures and their design implications. It was obvious after surveying the literature that the majority of the architectural work remains idiosyncratic and descriptions of network architectures are mostly idiomatic. This has led to the overloading of architectural terms, and to the emergence of a large body of network architecture proposals with no clear understanding of their cross similarities, compatibility points, their unique properties, and architectural performance and soundness. On the other hand, the second problem concerns the limitations of traditional naming and discovery schemes in terms of service differentiation and economic incentives. One of the recurring themes in the community is the need to separate an entity\u27s identifier from its locator to enhance mobility and security. Separation of identifier and locator is a widely accepted design principle for a future Internet. Separation however requires a process to translate from the identifier to the locator when discovering a network path to some identified entity. We refer to this process as identifier-based discovery, or simply discovery, and we recognize two limitations that are inherent in the design of traditional discovery schemes. The first limitation is the homogeneity of the service where all entities are assumed to have the same discovery performance requirements. The second limitation is the inherent incentive mismatch as it relates to sharing the cost of discovery. This dissertation addresses both subproblems, the architectural framework as well as the naming and discovery limitations

Innovation and new venture creation

[SPA] Crear lo "nuevo" para resolver problemas es una hazaña incierta. Aun así, el ser humano ha innovado y aplicado el ingenio durante milenios, llegando a crear nuevas herramientas, puentes y empresas, a pesar de la falta de recursos o de claridad en los objetivos. En este sentido, el problema de la asimetría de información (cómo se desplegará el futuro) y de la asimetría de recursos (de qué medios se dispondrá) motivó esta tesis. En particular, el problema de cómo los emprendedores crean nuevos emprendimientos e innovan bajo la incertidumbre y sin objetivos iniciales claros. Esta tesis pretende contribuir a la comprensión de la innovación y la creación de nuevos emprendimientos utilizando una lógica no predictiva (effectuation) y métodos ágiles (utilizados por las aceleradoras de startups) como principios orientadores de esta discusión. Effectuation es una lógica común aplicada por los emprendedores expertos para resolver los problemas típicos de la innovación y creación de nuevas empresas. Se trata de una heurística de control no predictiva que los emprendedores ponen en práctica a través de cinco principios de acción effectual al abordar las incertidumbres y sorpresas en la creación de nuevos productos, servicios o mercados: 1) Principio de "pájaro en mano": construyen un nuevo emprendimiento no necesariamente con un objetivo en mente, sino partiendo de sus propios medios y recursos (quiénes son, qué saben, a quienes conocen), 2) Principio de "pérdida asequible": no hacen grandes apuestas con la expectativa de obtener grandes beneficios, sino que evalúan las oportunidades en función de las desventajas aceptables, 3) Principio de "colcha loca": reducen la incertidumbre formando asociaciones y obteniendo compromisos iniciales en las primeras fases de sus nuevas empresas, 4) Principio de la “limonada”: aprovechan las contingencias en lugar de rechazarlas, permaneciendo flexibles y adaptando sus proyectos según sea necesario, 5) Principio del “piloto en el avión”: se centran en controlar lo que sea controlable en su entorno, entendiendo que el futuro no se encuentra ni se predice, sino que se hace a través de la acción humana. Las aceleradoras y los métodos ágiles activan los principios effectual a través de herramientas y prescripciones que reducen sistemáticamente las inversiones mientras se crea un nuevo emprendimiento. Las aceleradoras promueven ampliamente los métodos ágiles (por ejemplo, el modelo de desarrollo de clientes, los sprints de diseño, el ciclo de innovación rápida) para construir prototipos y primeras versiones de productos y servicios mientras se descubren los clientes y partners iniciales. Además, reduce el riesgo para los inversores en todas las fases de crecimiento de las startups al validar la idea del emprendimiento y aclarar qué recursos serán necesarios. En este sentido, esta tesis examinó si, y en qué medida, los emprendedores construyen nuevas empresas utilizando effectuation y métodos ágiles mediante la creación de tres innovaciones reales con aplicaciones en el mundo real. Los tres casos eran pruebas de concepto implementadas en contextos del mundo real con el objetivo explícito de lanzar Productos Mínimos Viables (Minimum Viable Products, MVP) pero bajo incertidumbre y con ambigüedad de objetivos sobre su funcionalidad. Las tres aplicaciones eran soluciones tecnológicas a problemas de congestión del tráfico, pandemias y confianza en las transacciones digitales. La aplicación 1, "Lemur", es una aplicación edge para el control del tráfico; la aplicación 2, "Dolphin", un sistema de geolocalización basado en sensores e Internet de las Cosas (Internet of Things, IoT) aplicado para el control de pandemias y la aplicación 3, "Crypto Degrees", una solución basada en blockchain para verificar títulos universitarios. En todas las etapas del desarrollo de cada aplicación, los equipos implicados la abordaron de forma emprendedora/eficaz, afrontando las incertidumbres y emprendiendo acciones para comprometerse con múltiples partes interesadas al tiempo que apalancaban las contingencias. Tras implementar las tres soluciones y analizar sus resultados e impacto, los tres casos validaron las predicciones teóricas de que, aplicando principios effectual de forma ágil, se pueden crear nuevos emprendimientos de forma emprendedora e innovadora. [ENG] Creating the "new" to solve problems is an uncertain feat. Still, humans have innovated and applied Ingenium for millennia, eventually creating new tools, bridges, and ventures, despite a lack of resources or clarity of objectives. In this sense, the problem of information asymmetry (how the future will deploy) and resource asymmetry (what means will be available) motivated this thesis. In particular, the problem of how entrepreneurs create new ventures and innovate under uncertainty and without clear initial goals. This thesis aims to contribute to understanding innovation and the creation of new ventures using a non-predictive logic (effectuation) and agile methods (used by startup accelerators) as guiding principles of this discussion. Effectuation is a common logic applied by expert entrepreneurs to solve the typical problems of starting new ventures and innovating. It is a non-predictive control heuristics entrepreneurs operationalize through five principles of effectual action while addressing the uncertainties and contingencies in creating new products, services or markets: 1) Bird-in-hand principle: they build a new venture not necessarily with a goal in mind, but starting with their own means and resources (who they are, what they know, who they know), 2) Affordable loss principle: they do not place large bets with the expectation of high returns, but rather assess opportunities based on acceptable downsides, 3) Crazy quilt principle: they reduce uncertainty by forming partnerships and gaining initial commitments early in their new ventures, 4) Lemonade principle: they leverage contingencies instead of rejecting them, remaining flexible and adapting their projects as required, 5) Pilot in the plane principle: they focus on controlling whatever is controllable in their environment, understanding that the future is not found or predicted, but it is made through human action. Accelerators and agile methods activate the effectual principles through tools and prescriptions that systematically reduce investments while creating a new venture. Accelerators extensively promote "agile" methods (e.g., customer development model, design sprints, rapid innovation cycle) to build prototypes and early versions Effectuation is a common logic applied by expert entrepreneurs to solve the typical problems of starting new ventures and innovating. It is a non-predictive control heuristics entrepreneurs operationalize through five principles of effectual action while addressing the uncertainties and contingencies in creating new products, services or markets: 1) Bird-in-hand principle: they build a new venture not necessarily with a goal in mind, but starting with their own means and resources (who they are, what they know, who they know), 2) Affordable loss principle: they do not place large bets with the expectation of high returns, but rather assess opportunities based on acceptable downsides, 3) Crazy quilt principle: they reduce uncertainty by forming partnerships and gaining initial commitments early in their new ventures, 4) Lemonade principle: they leverage contingencies instead of rejecting them, remaining flexible and adapting their projects as required, 5) Pilot in the plane principle: they focus on controlling whatever is controllable in their environment, understanding that the future is not found or predicted, but it is made through human action. Accelerators and agile methods activate the effectual principles through tools and prescriptions that systematically reduce investments while creating a new venture. Accelerators extensively promote "agile" methods (e.g., customer development model, design sprints, rapid innovation cycle) to build prototypes and early versions of products and services while discovering the initial customers and partners. Additionally, it reduces the risk for investors across all startup growth phases by validating the venture idea and clarifying what resources will be required. In this sense, this thesis examined whether and to what extent entrepreneurs build new ventures using effectuation and agile methods by creating three actual innovations with real-world applications. The three cases were proofs of concept implemented in real-world contexts with the explicit goal of launching Minimum Viable Products (MVPs) but under uncertainty and with ambiguity of objectives about its functionality. The three applications were technological solutions to problems of traffic congestion, pandemics, and trust in digital transactions. Application 1, "Lemur," is an edge application for traffic control; application 2, "Dolphin," an Internet of Things (IoT)-based geolocation system applied for pandemic control and application 3, "Crypto Degrees," a blockchainbased solution to verify university degrees. In all stages of each application development, the teams involved approached it in an entrepreneurial/effectual way, facing uncertainties and engaging in actions to engage with multiple stakeholders while leveraging contingencies. After implementing the three solutions and analyzing their results and impact, the three cases validated the theoretical predictions that by applying effectual principles in an agile form, new ventures can be created in an entrepreneurial, innovative way.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma Doctorado en Tecnologías de la Información y las Comunicacione

Systems for Challenged Network Environments.

Developing regions face significant challenges in network access, making even simple network tasks unpleasant and rich media prohibitively difficult to access. Even as cellular network coverage is approaching a near-universal reach, good network connectivity remains scarce and expensive in many emerging markets. The underlying theme in this dissertation is designing network systems that better accommodate users in emerging markets. To do so, this dissertation begins with a nuanced analysis of content access behavior for web users in developing regions. This analysis finds the personalization of content access---and the fragmentation that results from it---to be significant factors in undermining many existing web acceleration mechanisms. The dissertation explores content access behavior from logs collected at shared internet access sites, as well as user activity information obtained from a commercial social networking service with over a hundred million members worldwide. Based on these observations, the dissertation then discusses two systems designed for improving end-user experience in accessing and using content in constrained networks. First, it deals with the challenge of distributing private content in these networks. By leveraging the wide availability of cellular telephones, the dissertation describes a system for personal content distribution based on user access behavior. The system enables users to request future data accesses, and it schedules content transfers according to current and expected capacity. Second, the dissertation looks at routing bulk data in challenged networks, and describes an experimentation platform for building systems for challenged networks. This platform enables researchers to quickly prototype systems for challenged networks, and iteratively evaluate these systems using mobility and network emulation. The dissertation describes a few data routing systems that were built atop this experimentation platform. Finally, the dissertation discusses the marketplace and service discovery considerations that are important in making these systems viable for developing-region use. In particular, it presents an extensible, auction-based market platform that relies on widely available communication tools for conveniently discovering and trading digital services and goods in developing regions. Collectively, this dissertation brings together several projects that aim to understand and improve end-user experience in challenged networks endemic to developing regions.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91401/1/azarias_1.pd

The bridge of dreams::Towards a method for operational performance alignment in IT-enabled service supply chains

Concerns on performance alignment, especially on business-IT alignment, have been around for three decades. It is still considered to be one of the most important driving forces for business success, as well as one of the top concerns of many practitioners and organizational researchers. It is also found to be a major issue in two thirds of digital transformation projects. Many attempts from researchers in diverse disciplines have been made to tackle this issue. Unfortunately, they have been working separately and the research appears in various forms and names. This dissertation presents a piece of interdisciplinary research that focuses on identifying operational performance alignment issues, discovering and assessing their root causes with attention to the dynamics in operating IT-enabled service supply chain (SSC). It makes a modest contribution by providing a communication-centred instrument which can modularize complex SSC in terms of a hierarchically-structured set of services and analyze the performance causality between them. With a special focus on the impact of IT, it makes it possible to monitor and tune various performance issues in SSC. This research intends to provide a solution-oriented common ground where multiple service research streams can meet together. Following the framework proposed in this research, services, at different tiers of an SSC, are modelled with a balanced perspective on both business, technical service components and KPIs. It allows a holistic picture of service performances and interactions throughout the entire supply chain to be viewed through a different research lens and permits the causal impact of technology, business strategy, and service operations on supply chain performance to be unveiled

Generierung menschlicher Verhaltensprofile mittels unüberwachter Methoden zur Bewertung des Gesundheitszustandes

In the context of ambient assisted living, implementation of human behavior profiling is expected to occur through pervasive computing. As for information extraction from measured data, the typical way are supervised methods. However, due to the low adaptivity and high dependency on lab-setting, and the necessity of data labeling and model training, these types of methods are limited in human behavior profiling in real-life scenarios. Therefore, simple and unobtrusive sensors are relied upon to obtain daily behavior information. In spite of the incomplete observation, these sensors are able to provide key information. Thus, unsupervised methods have to be designed based on this measurement. In contrast to supervised data analysis, unsupervised methods have inherent advantages: Firstly, data labeling and training are not necessary. Secondly, they are more adaptive, making them suitable for use by different individuals. Thirdly, unknown knowledge might be discovered. In order to propose unsupervised methods for human behavior profiling that can be practically applied, the following research is conducted in this doctoral thesis: First, abstractions of events and patterns of in-home behavior scenario are defined. Second, the discovering algorithm is derived, whereby regularly occurring sensor events that can represent lifestyle patterns can be discovered. Third, with the lifestyle depicted, the change of human behavior is modeled to present the variance of lifestyle. Aiming to investigate the effectiveness of these methods, they are applied to the datasets obtained in GAL-NATARS study, which is carried out in the setting of real-life, and their effectiveness is evaluated through comparison with medical assessment results.Im Rahmen von Ambient Assisted Living sollen menschliche Verhaltensprofile durch den Pervasive Computing generiert werden. Zur Extraktion von Informationen aus Messdaten werden typischerweise überwachte Methoden verwendet. In Bezug sind diese Methoden wegen ihrer geringen Anpassungsfähigkeit, hohen Abhängigkeit von Laborumgebungen, der Notwendigkeit der Kennzeichnung und der Lernphase in realen Szenarien zur Generierung von menschliche Verhaltensprofile sehr eingeschränkt. Daher sollten einfache und unauffällige Sensoren verwendet werden, um täglich Verhaltensinformationen zu erhalten. Trotz der unvollständigen Beobachtung sind diese Sensoren in der Lage, die wichtige Informationen zu liefern. Hierfür sind unüberwachte Methoden notwendig, die auf der Grundlage dieser Messungen ausgeführt werden. Im Gegensatz zur überwachten Datenanalyse, haben unüberwachte Methoden folgende Vorteile: Zum einen sind keine Kennzeichnung von Daten und keine Lernphase erforderlich. Zweitens sind sie anpassungsfähiger, so dass sie für die Verwendung bei verschiedenen Individuen geeignet sind. Drittens können siebisher unbekanntes Wissen entdecken. Zur Entwicklung von praktisch anwendbaren unüberwachten Methoden für die Generierung menschlicher Verhaltensprofile, wird in dieser Doktorarbeit die folgende Forschung durchgeführt: Erstens, Definition von Abstraktionen für Ereignisse und Muster häuslichen Verhaltens. Zweitens wird ein Entdeckungsalgorithmus abgeleitet, der regelmäßig auftretende Sensorereignisse, die Lebensgewohnheiten darstellen können, entdecken kann. Drittens, wird mit den so gewonnenen Lebensgewohnheiten, die Änderung des menschlichen Verhaltens modelliert, um die Varianz des Lebensstils abzubilden. Mit dem Ziel, die Wirksamkeit dieser Methoden zu untersuchen, werden sie auf Datensätze aus dem Feld, gesammelt in der GAL-NATARS Studie durchgeführt wird, angewendet. Ihre Wirksamkeit wird durch den Vergleich mit den Ergebnissen der medizinischen Beurteilung bewertet

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

Semantically-enhanced advertisement recommender systems in social networks

El objetivo principal de la investigación es estudiar y diseñar un entorno de recomendación publicitaria en las redes sociales que puede ser enriquecido mediante tecnologías semánticas. A pesar de que existen muchas aplicaciones y soluciones para los sistemas de recomendación, en este estudio se diseña un framework robusto con un rendimiento adecuado para poder ser implementado en las redes sociales con el objetivo de ampliar los propósitos de negocio. De este objetivo principal se pueden derivar los siguientes objetivos secundarios: 1. Superar las limitaciones iniciales de los métodos clásicos de recomendación. 2. Aumentar la calidad y precisión de las recomendaciones y el rendimiento del sistema de recomendación. 3. Utilizar convenientemente la metodología planteada. 4. Establecer el marco propuesto en una plataforma de software real. 5. Considerar en la solución la portabilidad como un aspecto clave en los sistemas de software. 6. Considerar la fiabilidad del framework. 7. Tener un nivel de seguridad aceptable para el framework. En primer lugar, es necesario superar las limitaciones de los métodos clásicos de recomendación. En el presente trabajo, este objetivo se alcanzará mediante un método híbrido que se componga de los cuatro métodos básicos de recomendación (filtrado colaborativo, basado en contenido, demográfico y basado en conocimiento), y que recoja cada uno de los beneficios individuales de los mismos. En concreto, a pesar de los problemas conocidos de los métodos basados en filtrado colaborativo, a saber, escasez de datos (del inglés ‘data sparsity’), escalabilidad y arranque en frio (del inglés ‘cold start’), sigue siendo fundamental aprovechar las ventajas de esta técnica colaborativa de recomendación. Además, mediante la adición de técnicas semánticas durante el proceso de cálculo de las recomendaciones publicitarias, se aumentará la calidad y precisión de éstas. La tecnología semántica utilizada en el marco ha mejorado el rendimiento del sistema y supone un punto novedoso, siendo ésta una de las principales contribuciones frente al resto de investigaciones similares. En particular, para mejorar la exactitud de las recomendaciones, la semántica tanto de los distintos elementos de información como de los perfiles de clientes se ha tenido en cuenta. Introducir la semántica en el pronóstico proporciona una visión adicional sobre las explicaciones básicas detrás de las cuales un cliente podría permitir el acceso a productos específicos (algo que se entiende y se cubre con estrategias habituales sin consideración semántica). La semántica utilizada en este estudio es entendida en forma de relaciones entre conceptos. Como resultado, es posible extraer un conocimiento extra de los elementos disponibles. Otro de los objetivos de esta tesis es asegurar que se siga una metodología apropiada. Es necesario que la investigación obtenga resultados aceptables mediante la implementación de algoritmos fáciles de usar y un enfoque adecuado. Para alcanzar este objetivo, se diseña un caso de estudio, y posteriormente se implementa una aplicación Web capaz de determinar recomendaciones para los usuarios. El desarrollo de esta aplicación Web tiene sus propias dificultades y complejidades, pero la aplicación es amigable y fácil de usar. Los usuarios pueden navegar fácilmente en línea y trabajar con las aplicaciones instaladas en el sitio Web. La evaluación de la aproximación propuesta se realizará sobre este entorno real. De esta forma, también se establece como objetivo el establecer el framework en una plataforma de software real para probarlo y observar el rendimiento del mismo. Este objetivo es muy importante dado que si no existe la posibilidad de establecer un prototipo (prueba de concepto) para implementar la idea de la investigación, no será posible llegar a una conclusión adecuada y alcanzar los objetivos del estudio. Así, antes de desarrollar la idea de la investigación, se verificó si era posible encontrar una solución de software para obtener resultados reales en el marco implementado que permitiera posteriormente observar el resultado adecuado y, de este modo, asegurase de que los objetivos y requerimientos iniciales de la investigación en forma de resultados finales pueden ser probados. Asegurar la portabilidad y la fiabilidad es otra de las claves perseguidas en este trabajo. En este contexto, la portabilidad hace referencia a la posibilidad de implementar el framework en distintas plataformas disponibles incluyendo hardware, software, tipo de red social y publicidad. En este caso, el diseño del marco es independiente de cualquier plataforma. El framework se ha propuesto en un formato general y es muy fácil ajustarlo a los sistemas de software y hardware disponibles. Incluso es posible establecer el marco en diferentes sistemas operativos y no hay limitación en el número de instancias de instalación. Por otro lado, la fiabilidad, similar a la validez, es un método para evaluar la naturaleza de la estrategia de estimación utilizada para recopilar información en un estudio. En conjunto, para que los resultados de un estudio se consideren sustanciales, el sistema de estimación debe ser sólido. Lo que se persigue con la fiabilidad es que cualquier resultado crítico sea más que un hallazgo irregular y sea, por tanto, repetible. Distintos científicos deben tener la capacidad de realizar la misma investigación, en las mismas condiciones y producir los mismos resultados. Esto fortalecerá los descubrimientos y garantizará que grupos académicos más extensos reconozcan la teoría. La fiabilidad entendida de este modo es, en consecuencia, esencial para que una teoría se acumule como una verdad experimental reconocida. En esta tesis doctoral se realizan sobre la aplicación Web un total de 73 experimentos, resultando en un nivel prometedor de fiabilidad. Por último, la seguridad es uno de los retos fundamentales en las aplicaciones de la Web social y constituye un requisito básico del marco de trabajo propuesto en esta tesis. La seguridad es, en realidad, una de las principales preocupaciones de todas las aplicaciones software y la implementación del marco en una plataforma segura es, por tanto, muy importante. Para ello se consideró el componente de seguridad como uno de los elementos del marco, el cual se compone de diferentes niveles: (i) autenticación, y (ii) comprobación de identidad a partir del comportamiento. La autenticación única (‘SSO’ del inglés, Single Sign-On) permite a los usuarios loguearse en el sistema. Por otro lado, se mantiene un registro del comportamiento del usuario en las interacciones con la aplicación Web y se compara éste con el histórico. Este segundo nivel de seguridad previene el acceso de atacantes a contenidos no autorizados.The composition of Semantic Web advances with Web 2.0 application plan designs has risen to the social semantic Web, additionally introduced as Web 3.0. In accordance with this thought, a software platform will be displayed that effectively joins both Web 2.0 ideas and Semantic Web advancements. The structure of this study joins a progression of semantic-based application modules in a completely fledged social application with the goal of catching semantics in the purpose of information retrieval. Once the establishments and principle ideas of the alluded framework are brought up and its architecture was explained, a comprehensive model of the system will be demonstrated. Finally, the result of a case study will be validated using the standard metrics. It will be spoken to how the system can help in obtaining semantically-improved financially related data from the clients of the social applications and giving valuable proposals to advertisement recommender. The ability of knowledge contribution nowadays is unmatched ever. At no other time have such a large number of inventive and proficient individuals been associated by such a productive, all-inclusive system. The expenses of social occasion and registering over their commitments have come down to the point where new organizations with extremely humble spending plans give imaginative new administrations to a great number of online members. Collective intelligence is an amazing insight which can have numerous constructive outcomes on social networks. The outcome nowadays is amazing broadness of data and variety of point of view, and a society of mass investment that supports a wellspring of freely accessible substance. The Social Web (containing services, for example, MySpace, Flickr, last.fm, and WordPress) has caught the consideration of a large number of clients and in addition billions of dollars in venture and procurement. Social sites, advancing around the associations amongst individuals and their entities of interest, are experiencing limits in the territories of information integration, dispersal, reuse, compactness, searchability, automation and requesting undertakings like questioning. The Semantic Web is a perfect tool for interlinking and performing operations on various individual and item related information accessible from the Social Web, and has delivered an assortment of ways to deal with beat the limits being knowledgeable about Social Web application ranges. Recommendation is a compelling approach to diminish the expense for discovering data furthermore a capable approach to draw in clients. It has been broadly utilized as a part of numerous e-commerce applications, e.g., Amazon.com, CDNOW.com, eBay.com, Reel.com, et cetera. As of late, numerous techniques have been proposed for suggestion, for instance, Content-based Filtering, Collaborative Filtering, Clustering Model, Classification Model, Graph Model, and Association Rule approach. The proposed approaches have been connected to the conventional Web applications, which as a rule need suggest one and only sort of data (e.g., Amazon prescribes books, news.baidu.com prescribes news, and movielens.com prescribes films). So as to defeat data over-burden, recommender frameworks have turned into a key apparatus for giving clients customized suggestions on things, for example, films, music, books, news, and web pages. Captivated by numerous viable applications, analysts have created calculations and frameworks in the course of the most recent decade. Some of them have been popularized by online merchants, for example, Amazon.com, Netflix.com, and IMDb.com. These frameworks foresee user preferences (frequently spoke to as numeric evaluations) for new items in light of the client's past appraisals on different items. There are regularly two sorts of calculations for recommender frameworks - content-based techniques and collaborative filtering. Content-based techniques measure the likeness of the prescribed item (target item) to the ones that an objective user (i.e., user who gets recommendations) likes or aversions in light of item properties. Then again, collaborative filtering discovers users with tastes that are like the objective users depends on their ratings in the past. Collaborative filtering will then make recommendations to the objective user in light of the feelings of those comparative users. In spite of these endeavors, recommender frameworks still face numerous testing issues. These problems will make many limitations on the operation of recommendation systems. The change in the expectation precision can build client fulfillment, which thusly prompts higher benefits for those e-trade sites. Second, calculations for recommender frameworks experience the side effects of numerous problems. For instance, keeping in mind the end goal to gauge thing closeness, Content-based strategies depend with respect to express thing depictions. Be that as it may, such depictions might be hard to acquire for things like thoughts or feelings. As opposed to the tremendous number of things in recommender frameworks, every client regularly just rates a couple. In this way, the user/thing rating matrix is commonly extremely scanty. It is troublesome for recommender frameworks to precisely quantify client likenesses from those predetermined number of audits. A related issue is the Cold-start issue. Notwithstanding for a framework that is not especially meager, when a client at first joins, the framework has none or maybe just a couple audits from this client. In this manner, the framework can't precisely translate this current client's inclination. To handle those issues, two methodologies have been proposed. The main methodology is to gather the user/item rating matrix through dimensionality lessening systems, for example, Singular Value Decomposition (SVD). By grouping clients or things as per their idle structure, unrepresentative clients or things can be disposed of, and in this way the user/item grid gets to be denser. Nonetheless, these strategies don't essentially enhance the execution of recommender frameworks, and now and again aggravate the execution even. For using this approach, a methodology of kNN has been utilized for the framework to cluster users to two groups of neighbors and the other. So, the framework considers only those neighbor users which have more relative and similar data to the current user. The second approach is to "improve" the user/item rating matrix by 1) presenting default evaluations or verifiable client ratings, e.g., the time spent on perusing articles; 2) utilizing silly evaluating expectations from content-based techniques; or 3) abusing transitive relationship among clients through their past exchanges and feedback. These techniques enhance the execution of recommender frameworks to some degree. Specifically, another worldview of recommender frameworks is proposed by using data in social networks, particularly that of social impact. Customary recommender frameworks do not think about unequivocal social relations among clients, yet the significance of social impact in item advertising has for quite some time been perceived. Instinctively, when we need to purchase an item that is not commonplace, we frequently counsel with our companions who have as of now had involvement with the item, since they are those that we can go after quick exhortation. At the point when companions prescribe an item to us, we additionally have a tendency to acknowledge the suggestion in light of the fact that their inputs are dependable. This is one reason that collaborative filtering has been used as one of the components of the recommender system. Furthermore, the combination of social networks can hypothetically enhance the execution of current recommender frameworks. To start with, as far as the forecast precision, the extra data about clients and their companions acquired from social networks enhances the comprehension of client practices and appraisals. In this manner, we can demonstrate and translate client inclinations all the more absolutely, and accordingly enhance the forecast precision. Second, with companion data in social networks, it is no more important to discover comparable clients by measuring their rating comparability, in light of the fact that the way that two individuals are companions as of now demonstrates that they have things in like manner. In this manner, the information Sparsity issue can be reduced. At long last, for the Cold-start issue, regardless of the possibility that a client has no past audits, recommender framework still can make proposals to the client in view of the inclinations of his/her companions on the off chance that it coordinates with social networks. These instincts and perceptions rouse us to plan another worldview of recommender frameworks that can exploit data in social networks. The late rise of online social networks (OSNs) gives us a chance to examine the part of social impact in recommender frameworks. With the expanding ubiquity of Web 2.0, numerous OSNs, for example, Myspace.com, Facebook.com, and Linkedin.com have risen. Individuals in those systems have their own customized space where they not just distribute their life stories, leisure activities, interests, online journals, and so forth., additionally list their companions. Companions or guests can visit these individual spaces and leave remarks. OSNs give stages where individuals can put themselves on show and keep up associations with companions. As OSNs keep on gaining more fame, the phenomenal measure of individual data and social relations enhance sociology research where it was once constrained by an absence of information. As an exploration, the part of unequivocal social relations in recommender frameworks is as an important part of the research, for example, how client inclinations or evaluations are connected with those of neighbors, and how to utilize such relationships to outline a superior recommender framework. Specifically, a calculation structure is planned which makes suggestions taking into account client's own particular inclinations, the general acknowledgment of the objective thing, and the assessments from social networks. A genuine online social network data from last.fm has been crawled as a contextual investigation, and perform broad examination on this dataset. Additionally, the dataset is utilized, accumulated from the social network, to assess the execution of the proposed framework on the scalability, data sparsity, and cold start. The exploratory aftereffects of our framework show critical change against customary community oriented sifting in those perspectives. For instance, the computed precision in the wake of running the contextual analysis has enhanced by 0.7498 contrasted with conventional shared separating. Moreover, it is proposed to utilize the semantics of client connections by their similitudes and better grained client appraisals to enhance the expectation exactness

Planetary Science Vision 2050 Workshop : February 27–28 and March 1, 2017, Washington, DC

This workshop is meant to provide NASA’s Planetary Science Division with a very long-range vision of what planetary science may look like in the future.Organizer, Lunar and Planetary Institute ; Conveners, James Green, NASA Planetary Science Division, Doris Daou, NASA Planetary Science Division ; Science Organizing Committee, Stephen Mackwell, Universities Space Research Association [and 14 others]PARTIAL CONTENTS: Exploration Missions to the Kuiper Belt and Oort Cloud--Future Mercury Exploration: Unique Science Opportunities from Our Solar System’s Innermost Planet--A Vision for Ice Giant Exploration--BAOBAB (Big and Outrageously Bold Asteroid Belt) Project--Asteroid Studies: A 35-Year Forecast--Sampling the Solar System: The Next Level of Understanding--A Ground Truth-Based Approach to Future Solar System Origins Research--Isotope Geochemistry for Comparative Planetology of Exoplanets--The Moon as a Laboratory for Biological Contamination Research--“Be Careful What You Wish For:” The Scientific, Practical, and Cultural Implications of Discovering Life in Our Solar System--The Importance of Particle Induced X-Ray Emission (PIXE) Analysis and Imaging to the Search for Life on the Ocean Worlds--Follow the (Outer Solar System) Water: Program Options to Explore Ocean Worlds--Analogies Among Current and Future Life Detection Missions and the Pharmaceutical/ Biomedical Industries--On Neuromorphic Architectures for Efficient, Robust, and Adaptable Autonomy in Life Detection and Other Deep Space Missions