Expert systems / knowledge-based systems Books

Book SynopsisComputational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics.Trade ReviewFrom the reviews:“This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. … The writing in the book is quite clear and the author has done a good job providing the essence of each topic. … Overall, I think this is an excellent book. … This book will give a graduate student a good overview of the field. There are exercises provided for each chapter together with some solutions.” (Michael J. Evans, Mathematical Reviews, Issue 2011 b)“This book is a superior treatment of the important subject of statistical computing. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well. … All terms in this work including computing terms are clearly defined.” (Melvin Hinich, Technometrics, Vol. 53 (1), February, 2011)“I greatly appreciated the author’s command of both numerical and statistical computing … . The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. The target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. … I strongly recommend it for all scientific libraries.” (Soubhik Chakraborty, ACM Computing Reviews, October, 2010)“This book has a very large scope in that … it covers the dual fields of computational statistics and of statistical computing. … must-read for all students and researchers engaging into any kind of serious statistical programming. … is well-written, in a lively and personal style. … a reference book that should appear in the shortlist of any computational statistics/statistical computing graduate course as well as on the shelves of any researchers supporting his or her statistical practice with a significant dose of computing backup.”­­­ (Christian P. Robert, Statistical and Computation, Vol. 21, 2011)Table of ContentsPreliminaries.- Mathematical and Statistical Preliminaries.- Statistical Computing.- Computer Storage and Arithmetic.- Algorithms and Programming.- Approximation of Functions and Numerical Quadrature.- Numerical Linear Algebra.- Solution of Nonlinear Equations and Optimization.- Generation of Random Numbers.- Methods of Computational Statistics.- Graphical Methods in Computational Statistics.- Tools for Identification of Structure in Data.- Estimation of Functions.- Monte Carlo Methods for Statistical Inference.- Data Randomization, Partitioning, and Augmentation.- Bootstrap Methods.- Exploring Data Density and Relationships.- Estimation of Probability Density Functions Using Parametric Models.- Nonparametric Estimation of Probability Density Functions.- Statistical Learning and Data Mining.- Statistical Models of Dependencies.

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Book Synopsis Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisManaging and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy.Trade ReviewFrom the reviews:“This book provides a survey of some recent advances in graph mining. It contains chapters on graph languages, indexing, clustering, pattern mining, keyword search, and pattern matching. … The book is targeted at advanced undergraduate or graduate students, faculty members, and researchers from both industry and academia. … I highly recommend this book to someone who is starting to explore the field of graph mining or wants to delve deeper into this exciting field.” (Dimitrios Katsaros, ACM Computing Reviews, December, 2010)Table of ContentsAn Introduction to Graph Data.- Graph Data Management and Mining: A Survey of Algorithms and Applications.- Graph Mining: Laws and Generators.- Query Language and Access Methods for Graph Databases.- Graph Indexing.- Graph Reachability Queries: A Survey.- Exact and Inexact Graph Matching: Methodology and Applications.- A Survey of Algorithms for Keyword Search on Graph Data.- A Survey of Clustering Algorithms for Graph Data.- A Survey of Algorithms for Dense Subgraph Discovery.- Graph Classification.- Mining Graph Patterns.- A Survey on Streaming Algorithms for Massive Graphs.- A Survey of Privacy-Preservation of Graphs and Social Networks.- A Survey of Graph Mining for Web Applications.- Graph Mining Applications to Social Network Analysis.- Software-Bug Localization with Graph Mining.- A Survey of Graph Mining Techniques for Biological Datasets.- Trends in Chemical Graph Data Mining.

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Book SynopsisThis work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space.Table of ContentsVector Quantisation and Topology-Based Graph RepresentationGraph-Based Clustering AlgorithmsGraph-Based Visualisation of High-Dimensional Data

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Book SynopsisIn recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems (RTDBS), where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity.Table of ContentsList of Figures. List of Tables. Acknowledgments. Preface. Contributing Authors. I: Overview, Misconceptions and Issues. 1. Real-Time Database Systems: An Overview of System Characteristics and Issues; Tei-Wei Kuo, Kam-Yiu Lam. 2. Misconceptions About Real-Time Databases; J.A. Stankovic, et al. 3. Applications and System Characteristics; D. Locke. II: Real-Time Concurrency Control. 4. Conservative and Optimistic Protocols; Tei-Wei Kuo, Kam-Yiu Lam. 5. Semantics-Based Concurrency Control; Tei-Wei Kuo. 6. Real-Time Index Concurrency Control; J.R. Haritsa, S. Seshadri. III: Run-Time System Management. 7. Buffer Management in Real-Time Active Database Systems; A. Datta, S. Mukherjee. 8. Disk Scheduling; Ben Kao, R. Cheng. 9. System Failure and Recovery; R.M. Sivasankaran, et al. 10. Overload Management in RTDBs; J. Hansson, S.H. Son. 11. Secure Real-Time Transaction Processing; J.R. Haritsa, B. George. IV: Active Issues and Triggering. 12. System Framework of ARTDBs; J. Hansson, S.F. Andler. 13. Reactive Mechanisms; J. Mellin, et al. 14. Updates and View Maintenance; Ben Kao, et al. V: Distributed Real-Time Database Systems. 15. Distributed Concurrency Control; Ö. Ulusoy. 16. Data Replication and Availability; Ö. Ulusoy. 17. Real-Time Commit Processing; J.R. Haritsa, et al. 18. Mobile Distributed Real-Time Database Systems; Kam-Yiu Liam, Tei-Wei Kuo.VI: Prototypes and Future Directions. 19. Prototypes: Programmed Stock Trading; B. Adelberg, Ben Kao. 20. Future Directions; Tei-Wei Kuo, Kam-Yiu Lam. Index.

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Book SynopsisA deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods Analyze and extract insights from complex models from CNNs to BERT to time series models Book DescriptionInterpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.What you will learn Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers Use monotonic and interaction constraints to make fairer and safer models Understand how to mitigate the influence of bias in datasets Leverage sensitivity analysis factor prioritization and factor fixing for any model Discover how to make models more reliable with adversarial robustness Who this book is forThis book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.Table of ContentsTable of Contents Interpretation, Interpretability and Explainability; and why does it all matter? Key Concepts of Interpretability Interpretation Challenges Global Model-agnostic Interpretation Methods Local Model-agnostic Interpretation Methods Anchors and Counterfactual Explanations Visualizing Convolutional Neural Networks Interpreting NLP Transformers Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis Feature Selection and Engineering for Interpretability Bias Mitigation and Causal Inference Methods Monotonic Constraints and Model Tuning for Interpretability Adversarial Robustness What's Next for Machine Learning Interpretability?

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Book SynopsisThis book contains the thoroughly refereed technical papers presented in eight workshops collocated with the International Conference on Software Technologies: Applications and Foundations, STAF 2018, held in Toulouse, France, in June 2018. The 65 full papers presented were carefully reviewed and selected from 120 submissions. The events whose papers are included in this volume are: CoSim-CPS 2018: 2nd International Workshop on Formal Co-Simulation of Cyber-Physical Systems DataMod 2018: 7th International Symposium From Data to Models and Back FMIS 2018: 7th International Workshop on Formal Methods for Interactive Systems FOCLASA 2018: 16th International Workshop on Foundations of Coordination Languages and Self-adaptative Systems GCM 2018: 9th International Workshop on Graph Computation Models MDE@DeRun 2018: 1st International Workshop on Model-Driven Engineering for Design-Runtime Interaction in Complex Systems MSE 2018: 3rd International Workshop on Microservices: Science and Engineering SecureMDE 2018: 1st International Workshop on Security for and by Model-Driven Engineering Table of ContentsFormal Co-Simulation of Cyber-Physical Systems (CoSim-CPS).- From Data to Models and Back (DataMod).- Formal Methods for Interactive Systems (FMIS).- Foundations of Coordination Languages and Self-adaptative Systems (FOCLASA).- Graph Computation Models (GCM).- Model-Driven Engineering for Design-Runtime Interaction in Complex Systems (MDE@DeRun).- Microservices: Science and Engineering (MSE).- Security for and by Model-Driven Engineering (MDE).

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Book SynopsisThis open access book is about public open spaces, about people, and about the relationship between them and the role of technology in this relationship. It is about different approaches, methods, empirical studies, and concerns about a phenomenon that is increasingly being in the centre of sciences and strategies – the penetration of digital technologies in the urban space. As the main outcome of the CyberParks Project, this book aims at fostering the understanding about the current and future interactions of the nexus people, public spaces and technology. It addresses a wide range of challenges and multidisciplinary perspectives on emerging phenomena related to the penetration of technology in people’s lifestyles - affecting therefore the whole society, and with this, the production and use of public spaces. Cyberparks coined the term cyberpark to describe the mediated public space, that emerging type of urban spaces where nature and cybertechnologies blend together to generate hybrid experiences and enhance quality of life.Table of ContentsThe Unveiling Potential of Cyberparks.- Socio-Spatial Practices.- Programming and Activating Cyberparks.- Digital Hybrids - Between Tool and Methods.

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Book SynopsisThis open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.Table of ContentsMultivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder.- Dual Sequential Variational Autoencoders for Fraud Detection.- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks.- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams.- GraphMDL: Graph Pattern Selection Based on Minimum Description Length.- Towards Content Sensitivity Analysis.- Gibbs Sampling Subjectively Interesting Tiles.- Even Faster Exact k-Means Clustering.- Ising-Based Consensus Clustering on Special Purpose Hardware.- Transfer Learning by Learning Projections from Target to Source.- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs.- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces.- Vouw: Geometric Pattern Mining Using the MDL Principle.- A Consensus Approach to Improve NMF Document Clustering.- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.- Widening for MDL-Based Retail Signature Discovery.- Addressing the Resolution Limit and the Field of View Limit in Community Mining.- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics.- Adversarial Attacks Hidden in Plain Sight.- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code.- Overlapping Hierarchical Clustering (OHC).- Digital Footprints of International Migration on Twitter.- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks.- A Late-Fusion Approach to Community Detection in Attributed Networks.- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction.- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.- Actionable Subgroup Discovery and Urban Farm Optimization.- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model.- Detection of Derivative Discontinuities in Observational Data.- Improving Prediction with Causal Probabilistic Variables.- DO-U-Net for Segmentation and Counting.- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media.- Event Recognition Based on Classification of Generated Image Captions.- Human-to-AI Coach: Improving Human Inputs to AI Systems.- Aleatoric and Epistemic Uncertainty with Random Forests.- Master your Metrics with Calibration.- Supervised Phrase-Boundary Embeddings.- Predicting Remaining Useful Life with Similarity-Based Priors.- Orometric Methods in Bounded Metric Data.- Interpretable Neuron Structuring with Graph Spectral Regularization.- Comparing the Preservation of Network Properties by Graph Embeddings.- Making Learners (More) Monotone.- Combining Machine Learning and Simulation to a Hybrid Modelling Approach.- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.- Angle-Based Crowding Degree Estimation for Many-Objective Optimization.

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Book SynopsisThis open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions.Table of ContentsSupporting cross-domain system-level environmental and earth science.- ICT infrastructure for environmental and earth sciences.- Common challenges and requirements.- ENVRI reference model.- Reference model guided engineering.- Semantic and knowledge engineering using ENVRI RM.- Data curation and preservation.- Data cataloguing.- Data identification and citation.- Data processing.- Virtual infrastructure optimization.- Data provenance.- Metadata, semantic linking.- Authentication, Authorization, and Accounting.- Virtual research environment.- Case study: e.g., data subscriptions using elastic Cloud service.- Case study: e.g., D4Science: a VRE solution for RI.- Case study: LifeWatch.- Sustainability.- Future challenges.

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Book SynopsisThis textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail.The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified.The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.Table of ContentsPart I: Introduction to Visual Analytics in Data Science.- 1. Introduction to Visual Analytics by an Example.- 2. General Concepts.- 3. Principles of Interactive Visualisation.- 4. Computational Techniques in Visual Analytics.- Part II: Visual Analytics along the Data Science Workflow.- 5. Visual Analytics for Investigating and Processing Data.- 6. Visual Analytics for Understanding Multiple Attributes.- 7. Visual Analytics for Understanding Relationships between Entities.- 8. Visual Analytics for Understanding Temporal Distributions and Variations.- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation.- 10. Visual Analytics for Understanding Phenomena in Space and Time.- 11. Visual Analytics for Understanding Texts.- 12. Visual Analytics for Understanding Images and Video.- 13. Computational Modelling with Visual Analytics.- 14. Conclusion.

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Book SynopsisThis open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic.Table of ContentsThe New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows.- DBpedia Archivo - A Web-Scale Interface for Ontology Archiving under Consumer-oriented Aspects,. A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge.- Semantic Annotation, Representation and Linking of Survey Data.- QueDI: from Knowledge Graph Querying to Data Visualization.- EcoDaLo: Federating advertisement targeting with Linked Data.- MINDS: a translator to embed mathematical expressions inside SPARQL queries.- Integrating Historical Person Registers as Linked Open Data in the WarSampo Knowledge Graph.

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Book SynopsisWithout correct timing, there is no safe and reliable embedded software. This book shows how to consider timing early in the development process for embedded systems, how to solve acute timing problems, how to perform timing optimization, and how to address the aspect of timing verification.The book is organized in twelve chapters. The first three cover various basics of microprocessor technologies and the operating systems used therein. The next four chapters cover timing problems both in theory and practice, covering also various timing analysis techniques as well as special issues like multi- and many-core timing. Chapter 8 deals with aspects of timing optimization, followed by chapter 9 that highlights various methodological issues of the actual development process. Chapter 10 presents timing analysis in AUTOSAR in detail, while chapter 11 focuses on safety aspects and timing verification. Finally, chapter 12 provides an outlook on upcoming and future developments in software timing. The number of embedded systems that we encounter in everyday life is growing steadily. At the same time, the complexity of the software is constantly increasing. This book is mainly written for software developers and project leaders in industry. It is enriched by many practical examples mostly from the automotive domain, yet the vast majority of the book is relevant for any embedded software project. This way it is also well-suited as a textbook for academic courses with a strong practical emphasis, e.g. at applied sciences universities.Features and Benefits* Shows how to consider timing in the development process for embedded systems, how to solve timing problems, and how to address timing verification* Enriched by many practical examples mostly from the automotive domain* Mainly written for software developers and project leaders in industryTable of Contents1. General Basics.- 2. Microprocessor Technology Basics.- 3. Operating Systems.- 4. Timing Theory.- 5. Timing Analysis Techniques.- 6. Practical Examples of Timing Problems.- 7. Multi-Core, Many-Core, and Multi-ECU Timing.- 8. Timing Optimization.- 9. Methodology During the Development Process.- 10. AUTOSAR.- 11. Safety and ISO 26262.- 12. Outlook.

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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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