Expert systems / knowledge-based systems Books

Book SynopsisThis book constitutes revised selected papers from the International Workshops held at the Second International Conference on Process Mining, ICPM 2020, which took place during October 4-9, 2020. The conference was planned to take place in Padua, Italy, but had to be held online due to the COVID-19 pandemic.The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 29 papers included in this volume were carefully reviewed and selected from 59 submissions. They stem from the following workshops: 1st International Workshop on Event Data and Behavioral Analytics (EDBA) 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20) 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 3rd International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 1st International Workshop on Trust and Privacy in Process Analytics (TPPA) Table of Contents1st International Workshop on Event Data and Behavioral Analytics (EDBA).- Visually Representing History Dependencies in Event Logs.- Analysis of Business Process Batching using Causal Event Models.- Process Procespecting to Improve Renewable Energy Interconnection Queues: A Case Study.- Automated Discovery of Process Models with True Concurrency and Inclusive Choices.- A Novel Approach to Discover Switch Behaviours in Process Mining.- Process Model Discovery from Sensor Event Data.- Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study.- 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM).- Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-based Approach.- Time Matters:Time-Aware LSTMs for Predictive Business Process Monitoring.- A preliminary study on the application of Reinforcement Learning for Predictive Process Monitoring.- An Alignment Cost-Based Classi cation of Log Traces Using Machine-Learning.- Improving the Extraction of Process Annotations from Text with Inter-Sentence Analysis.- Case2vec: Advances in Representation Learning for Business Processes.- Supervised Conformance Checking using Recurrent Neural Network Classifiers.- 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20).- Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events.- Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation.- OTOSO: Online Trace Ordering for Structural Overviews.- Performance Skyline: Inferring Process Performance Models from Interval Events.- 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2020).- Alignment Approximation for Process Trees.- Stochastic Process Discovery By Weight Estimation.- Graph-based Process Mining.- Third International Workshop on Process-Oriented Data Science for Healthcare (PODS4H).- A Process Mining approach to statistical analysis: application to a real-world advanced melanoma dataset.- Process Mining of Disease Trajectories in MIMIC-III: A Case Study.- The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study.- Process mining on the extended event log to analyse the system usage during healthcare processes (Case study: the GP Tab usage during chemotherapy treatments).- Process Mining on FHIR - An Open Standards-Based Process Analytics Suite for Healthcare.- Deriving a sophisticated clinical pathway based on patient conditions from electronic health record data.- Exploration on How Global Warming Affects Emergency Services.- 1st Workshop on Trust and Privacy in Process Analytics (TPPA).- Towards Quantifying Privacy in Process Mining.

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Book SynopsisThis open access State-of-the-Art Survey describes and documents the developments and results of the Once-Only Principle Project (TOOP). The Once-Only Principle (OOP) is part of the seven underlying principles of the eGovernment Action Plan 2016-2020. It aims to make the government more effective and to reduce administrative burdens by asking citizens and companies to provide certain standard information to the public authorities only once.The project was horizontal and policy-driven with the aim of showing that the implementation of OOP in a cross-border and cross-sector setting is feasible. The book summarizes the results of the project from policy, organizational, architectural, and technical points of view. Table of ContentsThe Once-Only Principle: A Matter of Trust.- Implementation of the 'once-only' principle in Europe – national approaches.- Drivers for and Barriers to the Cross-Border Implementation of the Once-Only Principle - Once-Only Principle Good Practices in Europe.- The Single Digital Gateway Regulation as an Enabler and Constraint of Once-Only in Europe.- Legal Basis and Regulatory Applications of the Once-Only Principle: the Italian Case.- TOOP Trust Architecture.- The Technical challenges in OOP application across the European Union and the TOOP OOP architecture.- Testing methodology for the TOOP pilots.- TOOP pilot experiences: challenges and achievements in implementing once-only in different domains and Member States.- Measuring the Impact of the Once Only Principle for Businesses Across Borders.- The Future of the Once-Only Principle in Europe.

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Book SynopsisThis book focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories. The book contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. In addition, both a website and a Github account are also maintained for the book. They contain the code, data, and notebooks for the case studies; a summary of all the stories shared by the librarians/faculty; and hyperlinks to open an interactive virtual RStudio/Jupyter Notebook environment. The interactive virtual environment runs case studies based on the R programming language for hands-on practice in the cloud without installing any software. From understanding different types and forms of data to case studies showing the application of each text mining approaches on data retrieved from various resources, this book is a must-read for all library professionals interested in text mining and its application in libraries. Additionally, this book will also be helpful to archivists, digital curators, or any other humanities and social science professionals who want to understand the basic theory behind text data, text mining, and various tools and techniques available to solve and visualize their research problems. Table of Contents1. The Computational Library.- 2. Text Data and Where to Find Them?.- 3. Text Pre-Processing.- 4. Topic Modeling.- 5. Network Text Analysis.- 6. Burst Detection.- 7. Sentiment Analysis.- 8. Predictive Modeling.- 9. Information Visualization.- 10. Tools and Techniques for Text Mining and Visualization.- 11. Text Data and Mining Ethics.

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Book SynopsisThis open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty.Table of ContentsPart I: Foundations and Context.- 1. The Evolution of Data Spaces.- 2. How to Build, Run, and Govern Data Spaces.- 3. International Data Spaces in a Nutshell.- 4. Role of Gaia-X in the European Data Space Ecosystem.- 5. Legal Aspects of IDS: Data Sovereignty—What Does It Imply?.- 6. Tokenomics: Decentralized Incentivization in the Context of Data Spaces.- Part II: Data Space Technologies.- 7. The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange.- 8. Data Usage Control.- 9. Building Trust in Data Spaces.- 10. Blockchain Technology and International Data Spaces.- 11. Federated Data Integration in Data Spaces.- 12. Semantic Integration and Interoperability.- 13. Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning.- 14. IDS as a Foundation for Open Data Ecosystems.- 15. Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure.- Part III: Use Cases and Data Ecosystems.- 16. Silicon Economy: Logistics as the Natural Data Ecosystem.- 17. Agricultural Data Space.- 18. Medical Data Spaces in Healthcare Data Ecosystems.- 19. Industrial Data Spaces.- 20. Energy Data Space.- 21. Mobility Data Space.- Part IV: Solutions and Applications.- 22. Data Sharing Spaces: The BDVA Perspective.- 23. Data Platform Solutions.- 24. FIWARE for Data Spaces.- 25. Sovereign Cloud Technologies for Scalable Data Spaces.- 26. Data Space Based on Mass Customization Model.- 27. Huawei and International Data Spaces.- International Collaboration Between Data Spaces and Carrier Networks.- 29. From Linear Supply Chains to Open Supply Ecosystems.- 30. Data Spaces: First Applications in Mobility and Industry.- 31. Competition, Security, and Transparency: Data in Connected Vehicles.- Data Space Functionality.- The Energy Data Space: The Path to a European Approach for Energy.

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Book SynopsisThis book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments.To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution of Ordinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most. Table of ContentsPart I: Numerical Techniques.- 1. Introduction.- 2. Numerical Algorithms.- 3. Statistics.- 4. Linear Algebra.- 5. N-Dimensional Arrays.- 6. Ordinary Differential Equations.- 7. Signal Processing.- Part II: Advanced Data Analysis Techniques.- 8. Algorithmic Differentiation.- 9. Optimisation.- 10. Regression.- 11. Neural Network.- 12. Vector Space Modelling.- Part III: Use Cases.- 13. Case Study: Image Recognition.- 14. Case Study: Instance Segmentation.- 15. Case Study: Neural Style Transfer.- 16. Case Study: Recommender System.

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Book SynopsisDieses Buch bietet einen Überblick über Data-Mining-Methoden, die durch Software veranschaulicht werden. Beim Wissensmanagement geht es um die Anwendung von menschlichem Wissen (Erkenntnistheorie) mit den technologischen Fortschritten unserer heutigen Gesellschaft (Computersysteme) und Big Data, sowohl bei der Datenerfassung als auch bei der Datenanalyse. Es gibt drei Arten von Analyseinstrumenten. Die deskriptive Analyse konzentriert sich auf Berichte über das, was passiert ist. Bei der prädiktiven Analyse werden statistische und/oder künstliche Intelligenz eingesetzt, um Vorhersagen treffen zu können. Dazu gehört auch die Modellierung von Klassifizierungen. Die diagnostische Analytik kann die Analyse von Sensoreingaben anwenden, um Kontrollsysteme automatisch zu steuern. Die präskriptive Analytik wendet quantitative Modelle an, um Systeme zu optimieren oder zumindest verbesserte Systeme zu identifizieren. Data Mining umfasst deskriptive und prädiktive Modellierung. Operations Research umfasst alle drei Bereiche. Dieses Buch konzentriert sich auf die deskriptive Analytik.Das Buch versucht, einfache Erklärungen und Demonstrationen einiger deskriptiver Werkzeuge zu liefern. Es bietet Beispiele für die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Kapitel 1 gibt einen Überblick im Kontext des Wissensmanagements. Kapitel 2 erörtert einige grundlegende Softwareunterstützung für die Datenvisualisierung. Kapitel 3 befasst sich mit den Grundlagen der Warenkorbanalyse, und Kapitel 4 demonstriert die RFM-Modellierung, ein grundlegendes Marketing-Data-Mining-Tool. Kapitel 5 demonstriert das Assoziationsregel-Mining. Kapitel 6 befasst sich eingehender mit der Clusteranalyse. Kapitel 7 befasst sich mit der Link-Analyse. Die Modelle werden anhand geschäftsbezogener Daten demonstriert. Der Stil des Buches ist beschreibend und versucht zu erklären, wie die Methoden funktionieren, mit einigen Zitaten, aber ohne tiefgehende wissenschaftliche Referenzen. Die Datensätze und die Software wurden so ausgewählt, dass sie für jeden Leser, der über einen Computeranschluss verfügt, weithin verfügbar und zugänglich sind.Table of Contents

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Book Synopsis.- Supercomputer Simulation..- Born Approximation and Transfer Learning to Accelerate the Training Stage in Data-Driven End-to-End Approach for Seismic Monitoring in Viscoelastic Media..- Coarray Fortran Implementation of the TRM Tunnel Boundary Detection AlgorithmAnastasia Galaktionova and Galina Reshetova..- Comparison the Decomposition and Partitioning Approaches of Large Number of Boundary-Conforming Grids Covered Fractured Geological Media..- Docking and Post-processing of 1 Million Molecules from the CNCL Database in Search of SARS-CoV-2 Mpro Inhibitors..- Domain Decomposition for the Numerical Solution of the Cahn-Hilliard Equation..- Efficient Parallel Computing for Dynamic Free Surface Flows: A Study with FLOW-3D..- Microwave Tomography Method for Determining Inhomogeneities in the Inverse Diffraction Problem..- Numerical Simulation of the Laser Pulse Propagation in Thin Cloud Layers..- On the Problems of Convergence of Iterative Methods for Solving Two-Coefficient Inverse Problems of Ultrasound Tomography..- Parallel Algorithms for Calculating Problems of Supersonic Cold Gas-Dynamic Spraying Nanoparticles on Substrates..- Parallel Algorithms for Solving Mass Transfer Equations in the Fracture Set Matrix System..- Parallel Efficiency Analysis of Reactive Transport Simulations Using the GeRa Software..- Performance of parallel NetCDF output in the INM RAS Earth system model..- Quantum-Chemical Calculations of the Enthalpy of Formation of Isomeric 5/6/5 Tricyclic Tetrazolotetrazine Derivatives Annelated with Nitroazoles..- Simulating the Black Sea 7Be Transport with Nested General Circulation Models..- Stresses in Thin Optical Films: Results of Highperformance Atomistic Simulation..- The Influence of the Kelvin-Helmholtz Instability on the Shape and Decay of Molecular Clouds Remnants Moving behind the Shock Wave after a Supernova Explosion..- The Numerical Dispersion Mitigation in Three-Dimensional Wavefields..- Towards an Adaptation of the Nonlinear Harmonics Method Realized in an Unstructured Flow Solver for Simulation of Turbomachinery Problems on Supercomputers..- Using the MULTICOMP Package to Predict the Properties of Polymer-based Materials..- HPC, BigData, AI: Algorithms, Technologies, Evaluation..- A Study of a Composable Approach to Parallel Programming for Many-core Multiprocessors..- A Versatile Simulator for Complex Cluster Workloads..- An Explanation Method for Semantic Segmentation Enhance Brain Tumor Classification..- Benchmarking Deep Learning Inference on RISC-V CPUs..- Evaluation and Prediction of Human Software Developers' Perception of Large Language Models Suggestions Using GitHub Data..- Incomplete factorization approach in algebraic domain decomposition methods..- Job Mapping Cyclic Composite Algorithm for Supercomputer Resource Manager..- OpenMP Parallel Efficiency for DFM Flow and Transport Model Coupled with PrecipitationDissolution Reactions..- Predicting Characteristics of Salmon Return Migration Using Machine Learning Models..- Study of OpenCL-based neural network convolutions on GPUs..- Supervised and Transfer Learning for Phase Transition Research..- The Energy Efficiency Research of Code for Numerical Simulation of Plasma Physics Problems.

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Book Synopsis.- Distributed Computing..- A Comprehensive, Scalable and Fast BOINC Simulator..- Bi-Objective Workflow Scheduling in the Cloud: What is the Real State-of-the-Art?..- Characterization of a Desktop Grid Project as a Queueing System..- Desktop Grid Based Assessment of the Game-Theoretical Model of Regional Digitalization Support in Aquaculture..- Efficient Resource Selection in Cloud Environments with Volume Discounts and Group Dependencies..- Experiments with the A022008 Sequence Generator to Study Distributed Computing Based on State Synchronization Service..- Fast and Flexible Framework for Simulation of Distributed Systems..- Probabilistic Models of the Behavior of the BOINC Infrastructure in Typical Situations..- Simulation of Volunteer Computing in a Desktop Grid System..- HPC Education..- Applied Discrete Optimization in the Development of the Discipline "Computational Methods"..- New Approach to Studying the Concept of Information for IT-students.

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Book SynopsisIntroduction to Parallel Computing: Architectures and Models, Algorithms and Measures.- Shared Memory Parallel Systems and OpenMP.- Distributed Memory Parallel Systems and MPI.- Appendix A, Proofs and Supplementary Material.- References.- Index.

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Book Synopsis.- Multiuser HAP Architecture using Symbol Wavelengths..- 5G NR Waveform Application in Bistatic Inverse Synthetic Aperture Radars..- Radar detection in the presence of impulse interference..- Ionospheric response to the most powerful storm of solar cycle 25 in May 2024..- UAV Detection and Recognition Technologies..- Digital System for Registering Emergency Events in Electric Vehicles..- Modern Technological Solutions for Passive Buildings and Buildings with Zero Energy Consumption..- Drone Technology and External Contextual Factors.

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Book SynopsisSystem Safety Assessment.- Failure and defect detection of safety critical 3D printed goods.- Model-Based Safety Assessment for Flight Control Systems: Methodology and Case Study.- Multi-approach based Safety Analysis of a Wastewater Treatment System.- Application of a MBSA approach on a representative subsystem of EGNOS (European Geostationary Navigation Overlay Service).- Safety Analysis Methods in Aerospace: A Case-Based Comparison of FTA and MBSA.- Cybersecurity Analysis.- MBCA: A Model-Based Approach for Cybersecurity Analysis of Cyber-Physical Systems.- Cybersecurity Threat Detection through Business Process Log Analysis.- Interpretable and Trustworthy Attack Diagnosis for UAVs Using SafeML.- Safe Machine Learning.- Incorporating failure of Machine Learning in probabilistic safety assessment and runtime safety assurance.- Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML.- CODIF: Counterfactual data-augmentations for estimating perception influencing factors.- The Information Meta Model for Machine Learning IM3L: A Structured Approach to ML Integration in Engineering Systems.- RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs.- Probabilistic Analysis.- Variance-based Sensitivity Analysis for Probabilistic Risk Assessment.- Causal Bayesian Networks for Data-driven Safety Analysis of Complex Systems.- Model-based Design and Safety Assessment.- From Natural Language Requirement Specifications to Logic Properties.- Model-Based Dependent Failure Analysis.- Comparative Analysis of Non-Colored and Colored Petri Net Models for Availability Assessment of Safety-Critical Cloud Software in Railways.- MBSA model exchange and its challenges.- ACEditor: a Modeling Tool for Synthesizing Exceutable Assurance Cases from Fault Trees.- Machine Learning and Automata Learning for System Safety.- AI4Green, A Framework for AI-based Resource Optimizations for Reliable Applications.- Analyzing Truck Platoons with Automata Learning and Model Checking.- Q-SafeML, A Quantum-Statistical Approach to Safety Monitoring in Quantum Machine Learning.- Failure Detection Isolation and Recovery Analysis.- Towards a Unifying View of Fault Propagation Analyses and Notations.- An Altarica-based modelling and analysis approach enabling UAV regulation compliance.- Timed Models in AltaRica 3.0.- Experience in developing an algorithm at the MBSA level to minimize the complexity of fault trees during automatic generation from design data.- From Abstract to Action: Tailored Environment Taxonomies for More Complete ADS Safety Analyses.

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Book SynopsisData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.Trade Review"The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years." William E. Winkler, U.S. Bureau of the Census, Washington, DC, USATable of ContentsPart I Overview.- Introduction.- The Data Matching Process.- Part II Steps of the Data Matching Process.- Data Pre-Processing.- Indexing.- Field and Record Comparison.- Classification.- Evaluation of Matching Quality and Complexity.- Part III Further Topics.- Privacy Aspects of Data Matching.- Further Topics and Research Directions.- Data Matching Systems.

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Book SynopsisEmbedded systems have long become essential in application areas in which human control is impossible or infeasible. The development of modern embedded systems is becoming increasingly difficult and challenging because of their overall system complexity, their tighter and cross-functional integration, the increasing requirements concerning safety and real-time behavior, and the need to reduce development and operation costs.This book provides a comprehensive overview of the Software Platform Embedded Systems (SPES) modeling framework and demonstrates its applicability in embedded system development in various industry domains such as automation, automotive, avionics, energy, and healthcare. In SPES 2020, twenty-one partners from academia and industry have joined forces in order to develop and evaluate in different industrial domains a modeling framework that reflects the current state of the art in embedded systems engineering.The content of this book is structured in four parts. Part I “Starting Point” discusses the status quo of embedded systems development and model-based engineering, and summarizes the key requirements faced when developing embedded systems in different application domains. Part II “The SPES Modeling Framework” describes the SPES modeling framework. Part III “Application and Evaluation of the SPES Modeling Framework” reports on the validation steps taken to ensure that the framework met the requirements discussed in Part I. Finally, Part IV “Impact of the SPES Modeling Framework” summarizes the results achieved and provides an outlook on future work.The book is mainly aimed at professionals and practitioners who deal with the development of embedded systems on a daily basis. Researchers in academia and industry may use it as a compendium for the requirements and state-of-the-art solution concepts for embedded systems development.Table of ContentsPart I Starting Situation.- Challenges in Engineering for Software-Intensive Embedded Systems.- Requirements from the Application Domains.- Part II The SPES Modeling Framework.- Introduction to the SPES Modeling Framework.- Requirements Viewpoint.- Functional Viewpoint.- Logical Viewpoint.- Technical Viewpoint.- Modeling Quality Aspects: Safety.- Modeling Quality Aspects: Real-Time.- Part III Application and Evaluation of the SPES Modeling Framework.- Overview of the SPES Evaluation Strategy.- Application and Evaluation in the Automation Domain.- Application and Evaluation in the Automotive Domain.- Application and Evaluation in the Avionics Domain.- Application and Evaluation in the Energy Domain.- Application and Evaluation in the Healthcare Domain.- Evaluation Summary.- Part IV Impact of the SPES Modeling Framework.- Lessons Learned.- Outlook.

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Book SynopsisDas Internet bietet Unternehmen und Kunden ganz neue Kommunikationsmöglichkeiten. Via Corporate-Website der Unternehmen können sich Kunden 24 Stunden am Tag mit Informationen über das Unternehmen versorgen. Dies ermöglicht Unternehmen direkten Einfluss auf ihre Kunden auszuüben und gleichzeitig umfangreiche Informationen über diese zu sammeln. Auf der anderen Seite belastet der Anspruch der permanenten Verfügbarkeit zur Gewährleistung eines optimalen Kundenservices das Kundenservice-Potential der Unternehmen. Chatbots entlasten Call-Center und Customer-Support-Abteilungen, da sie bereits 80 % der gestellten Fragen direkt via Website beantworten können. Sie sind 24 Stunden am Tag verfügbar, Kosten werden reduziert. Ferner wird den Benutzern der Umgang mit der Website erleichtert. Die automatisch dokumentierten Unterhaltungen liefern zudem konkrete Einblicke in den tatsächlichen Informationsbedarf der Kunden.Table of Contents1 Einleitung.- 1.1 Zustandsbeschreibung.- 1.2 Problembeschreibung.- 1.3 Problemanalyse.- 1.3.1 Sprachproblematik.- 1.3.2 Divergenz der Bedürfnisse.- 1.3.3 Komplexität verteilter Systeme.- 1.4 Lösungsansatz.- 1.5 Abgrenzung des Themas.- 1.6 Vorgehen.- 2 Grundlagen.- 2.1 Begriffsabgrenzung.- 2.1.1 User Interface.- 2.1.2 Usability.- 2.1.3 Agenten.- 2.1.4 Chatbot.- 2.1.5 Künstliche Intelligenz.- 2.2 Historische Entwicklung.- 2.2.1 Aufweichung der Grenze Mensch-Maschine.- 2.2.2 Human-Computer Interaction (HCI).- 2.2.3 Entwicklung von Chatbots.- 2.2.4 Chatbots im Zentrum der Kritik.- 2.3 Warum Chatbots?.- 2.3.1 Interaktion in natürlicher Sprache.- 2.3.2 Aktive Gesprächsführung.- 2.3.3 Informationsstrukturierung.- 2.3.4 Realisierung von Einsparungspotenzialen.- 2.3.5 Gewinnung von Kundeninformationen.- 2.3.6 Gesteigerte Zugänglichkeit.- 2.4 Formen von Chatbots.- 2.4.1 Verständnisebenen von Chatbots.- 2.4.2 Beispiele.- 3 Gestaltungsgrundsätze.- 3.1 Implikationen adaptiver Funktionalität.- 3.2 Natürliche Sprache.- 3.3 Repräsentierung.- 3.3.1 Tool oder Team?.- 3.3.2 Höflichkeit.- 3.3.3 Komplimente.- 3.3.4 Form der Repräsentierung.- 3.3.5 Persönlichkeit.- 3.3.6 Spezialisten.- 3.3.7 Geschlechts-Stereotypen.- 3.3.8 Verwendung von Stimmen / Personifizierung.- 3.3.9 Bewegung.- 3.3.10 Synchronismus.- 3.4 Glaubhaftigkeit und Vertrauenswürdigkeit.- 3.5 Konzeptionelle Implementierungsrichtlinien.- 3.5.1 Klare Kommunikation.- 3.5.2 Anreize schaffen.- 3.5.3 Konsistenten Multichannel-Kontakt ermöglichen / Integration.- 3.5.4 Kontinuierliche Weiterentwicklung.- 4 Evaluation.- 4.1 Moralische Einwände.- 4.1.1 Evaluation der Kritik.- 4.1.2 Folgerungen.- 4.2 Verständnisproblematik: Kontext und Gesunder Menschenverstand.- 4.2.1 Evaluation der Kritik.- 4.2.2 Folgerungen.- 4.3 Personifizierte Interfaces.- 4.3.1 Evaluation der Kritik.- 4.3.2 Folgerungen.- 4.4 Ineffizienz indirekter Manipulation.- 4.4.1 Evaluation der Kritik.- 4.4.2 Folgerungen.- 4.5 Erkenntnisse aus dem Praxiseinsatz.- 4.5.1 Coca-Cola.- 4.5.2 Defense Logistics Information Service.- 4.5.3 Deutsche Direktbank.- 4.5.4 Deutscher Getränkefabrikant.- 4.5.5 Direkt Anlage Bank.- 4.5.6 Ford.- 4.5.7 Hannoversche Lebensversicherung.- 4.5.8 Interact Commerce.- 4.5.9 Net-tissimo.com.- 4.5.10 One2One.- 4.5.11 Pioneer Investment.- 4.5.12 Schwäbisch-Hall.- 4.5.13 Zusammenfassung.- 5 Anwendungsszenarien.- 6 Ausblick.- 7 Anhang.- 7.1 Übersicht: Anbieter, Anwender, Forschungsgebiete.- 7.2 Interviews.- 7.2.1 Coca-Cola.- 7.2.2 Defense Logistics Information Service.- 7.2.3 Deutsche Direktbank.- 7.2.4 Deutscher Getränkefabrikant.- 7.2.5 Direkt Anlage Bank.- 7.2.6 Hannoversche Lebensversicherung.- 7.2.7 Net-tissimo.com.- 7.2.8 Pioneer Investment.- 7.2.9 Schwäbisch-Hall.- 7.3 AIML-Spezifikation.- 7.3.1 Grundaufbau AIML.- 7.3.2 Reduktion.- 7.3.3 SRAI.- 7.3.4 Lernen (Think).- 7.3.5 Variablen.- 7.3.6 Eigenschaften (Predicates).- 7.3.7 Scripts.- Literatur.

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Book SynopsisComputational Creativity, Concept Invention, and General Intelligence in their own right all are flourishing research disciplines producing surprising and captivating results that continuously influence and change our view on where the limits of intelligent machines lie, each day pushing the boundaries a bit further. By 2014, all three fields also have left their marks on everyday life – machine-composed music has been performed in concert halls, automated theorem provers are accepted tools in enterprises’ R&D departments, and cognitive architectures are being integrated in pilot assistance systems for next generation airplanes. Still, although the corresponding aims and goals are clearly similar (as are the common methods and approaches), the developments in each of these areas have happened mostly individually within the respective community and without closer relationships to the goings-on in the other two disciplines. In order to overcome this gap and to provide a common platform for interaction and exchange between the different directions, the International Workshops on “Computational Creativity, Concept Invention, and General Intelligence” (C3GI) have been started. At ECAI-2012 and IJCAI-2013, the first and second edition of C3GI each gathered researchers from all three fields, presenting recent developments and results from their research and in dialogue and joint debates bridging the disciplinary boundaries. The chapters contained in this book are based on expanded versions of accepted contributions to the workshops and additional selected contributions by renowned researchers in the relevant fields. Individually, they give an account of the state-of-the-art in their respective area, discussing both, theoretical approaches as well as implemented systems. When taken together and looked at from an integrative perspective, the book in its totality offers a starting point for a (re)integration of Computational Creativity, Concept Invention, and General Intelligence, making visible common lines of work and theoretical underpinnings, and pointing at chances and opportunities arising from the interplay of the three fields.Table of ContentsStakeholder Groups in Computational Creativity Research and Practice.- Weak and Strong Computational Creativity.- Theorem: General intelligence entails creativity, assuming.- The Computational Creativity Complex.- How Models of Creativity and Analogy Need to Answer the Tailorability Concern.- On the role of computers in creativity-support systems.- A computational theory of creativity as everyday reasoning from learned information.- Accounting for creativity within a psychologically realistic cognitive architecture.- E pluribus unum - Formalisation, Use-Cases, and Computational Support for Conceptual Blending.- Creating Meaningful and Poetic Instances of Rhetorical Forms.- Open-Ended Elaborations in Creative Metaphor.- Poetry generation with PoeTryMe.- From MEXICA to MEXICA-impro: the Evolution of a Computer Model for Plot Generation.- Handle: Engineering Artificial Musical Creativity at the "Trickery" Level.- A Culinary Computational Creativity System.- Interactive Meta-Reasoning: Toward a CAD-like environment for designing game-playing agents.- Collective Discovery Events: Web-based Mathematical Problem-solving with Codelets.- A Personal Perspective Into the Future for Computational Creativity.

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