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• Intro; Title Page; Contents; Introduction; Motivation; Problem Statement; Challenges and Overview of the State-of-the-Art; Challenges for Efficient SPARQL Query Processing; Challenges for Effective SPARQL Query Processing; Hypotheses and Research Questions; Contributions; Outline; Foundations of Linked Data Management; Linked Data; The Resource Description Framework (RDF); Querying RDF Data: The SPARQL Query Language; Querying RDF Data on the Web; URI Dereferencing and Link Traversal; SPARQL Endpoints; Linked Data Fragments; Foundations of Query Processing; Query Optimization

• Adaptive Query ProcessingAdaptive Query Processing over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Adaptive Link Traversal Approaches; Adaptive Query Processing Against SPARQL Endpoints; Query Processing Approaches Against TPF Servers; The nLDE Approach; nLDE Query Optimizer; Estimation of Query Plan Cardinalities; Placing Physical Operators; Building Query Tree Plans; nLDE Optimizer: Algorithm Description; Complexity of the nLDE Query Optimizer; nLDE Adaptive Routing Query Engine; Adaptive Operators; Eddies

• Network of Linked Data Eddies (nLDE)Termination of nLDE; Correctness of nLDE; Routing Policies; Routing Policy from Eddies to Adaptive Operators; Routing Policy from Adaptive Operators to Eddies; Experimental Study; Experimental Settings; Efficiency of the nLDE Optimizer; Effectiveness of the nLDE Optimizer; Impact of the nLDE Routing-based Adaptivity on Execution Time in Perfect Networks; Effectiveness of the nLDE Routing-based Adaptivity Under the Presence of Network Delays; Summary and Future Work; Foundations of Crowdsourcing; Overview; Types of Crowdsourcing; Microtasks; Contests

• Crowdsourcing WorkflowsHybrid Crowdsourcing Workflows; Human-based Workflow: Find-Fix-Verify; Crowdsourcing Query Answer Completeness over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Hybrid Query Processing for Relational Data; Crowd-based Linked Data Management Applications; Web Data Quality Assessment; The HARE Approach; Problem Definition; Proposed Solution; RDF Completeness Model; Representation of the Crowd Knowledge; Crowd Contradiction; Crowd Unknownness; HARE Microtask Manager; User Interface Generator

• Microtask ExecutorHARE Query Optimizer; Complexity of the HARE Query Optimizer; HARE Query Engine; A SPARQL Fuzzy Set Semantics; HARE BGP Executor; Complexity of HARE Query Evaluation; Experimental Study; Experimental Settings; HARE Crowdsourcing Capabilities; Size of Query Answer; Quality of Crowd Answers; Crowd Response Time; Summary and Future Work; Crowdsourcing Linked Data Quality Issues; Introduction; Research Questions; Contributions; Structure of the Chapter; Preliminaries: Linked Data Quality Issues; Related Work; Crowdsourcing in Linked Data Management; Web Data Quality Assessment

For any Microsoft Office user familiar with Word, Excel, PowerPoint, Access, or Outlook, this book provides tutorials on the tools and tricks that automate these programs for increased productivity. Each lesson includes instructions and a downloadable file on which to test the VBA macros and procedures provided. Tasks covered include how to run several mail merges at once, removing duplicates from data, and saving email attachments.

• Financial awareness.- Financial education.- Financial forecasting.- Crowdsourcing.- Recommendation and engagement tools.- Development of online platforms.- Air quality monitoring by online platforms.- Measuring and publishing outdoor air pollution levels by online platforms.- Leveraging the power of online social networks, mobile and open hardware technologies, and engagement strategies.
• (source: Nielsen Book Data)

This book contains the papers presented at the two CAPS (Collective Awareness Platforms for Sustainability and Social Innovation) workshops, namely the First International Workshop on the Internet for Financial Collective Awareness and Intelligence, IFIN 2016, and the First International Workshop on Internet and Social Media for Environmental Monitoring, ISEM 2016, held in Florence, Italy in September 2016. The two workshops were collocated with the third International Conference on Internet Science, INSCI 2016. The 8 papers presented have been carefully reviewed and selected from 13 submissions. The papers of the two workshops although targeting different goals aim at developing platforms promoting awareness on different but critical sustainability issues.
(source: Nielsen Book Data)

• Introduction
• The challenges of automated item picking: the last mile of logistics for e-commerce
• Robotic Sensing for Item Picking
• Gripper Design and Grasping Strategies
• Machine Learning for Item Identification and Pose Estimation
• Machine Learning for Motion Planning
• Efficient Task Planning Strategies.

This book is a compilation of advanced research and applications on robotic item picking and warehouse automation for e-commerce applications. The works in this book are based on results that came out of the Amazon Robotics Challenge from 2015-2017, which focused on fully automated item picking in warehouse setting, a topic that has been assumed too complicated to solve or has been reduced to a more tractable form of bin picking or single-item table top picking. The books contributions reveal some of the top solutions presented from the 50 participant teams. Each solution works to address the time-constraint, accuracy, complexity, and other difficulties that come with warehouse item picking. The book covers topics such as grasping and gripper design, vision and other forms of sensing, actuation and robot design, motion planning, optimization, machine learning and artificial intelligence, software engineering, and system integration, among others. Through this book, the authors describe how robot systems are built from the ground up to do a specific task, in this case, item picking in a warehouse setting. The compiled works come from the best robotics research institutions and companies globally. Presents an inside look at the various solutions for automated warehouse item picking based on the Amazon Robotics Challenge (ARC) Contains details of the challenges and solutions involved in automating item picking Provides details and insights on the solutions of the winning teams Includes chapters written by scientists and engineers at the forefront of robotics research.

• The world, knowledge, and models
• Translating experience
• Modeling and mining : putting it together
• Framing business models
• Getting the right model
• Getting the model right
• Deploying the model
• Getting started
• What mining tools do
• Getting the initial model : basic practices of data mining
• Improving the model
• Deploying the mined model
• Methodology.

Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. Teaches how to discover, construct and refine models that are useful in business situations Teaches how to design, discover and develop the data necessary for mining Provides a practical approach to mining data for all business situations Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.

• Part I, From Business Problems to Data Science.- Data Science Based on Artificial Intelligence.- Business Problems Dependent on Data.- Artificial Intelligence-Based Data Science Solutions.- Integrate and Conquer.- The Lost-in-Translation Trap.- Part II, The AI-Based Data Science Toolbox.- The AI-Based Data Science Workflow.- Problem Knowledge Acquisition.- Data Preparation.- Data Analysis.- Model Development.- The Model Deployment Life Cycle.- Part III, AI-Based Data Science in Action.- Infrastructure.- People.- Applications of AI-Based Data Science in Manufacturing.- Applications of AI-Based Data Science in Business.- How to Operate AI-Based Data Science in a Business.- How to Become an Effective Data Scientist.- Glossary.
• (source: Nielsen Book Data)

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.
(source: Nielsen Book Data)

• Blockchain Forum.- Silver Bullet for all Trust Issues? Blockchain-based Trust Patterns for Collaborative Business Processes.- Blockchain Oracles: A Framework for Blockchain-Based Applications.- Foundational Oracle Patterns: Connecting Blockchain to the Off-chain World.- The Role of Modeling in Blockchain Process Design.- External Data Monitoring Using Oracles in Blockchain-Based Process Execution.- Robotic Process Automation Forum.- A Conversational Digital Assistant for Intelligent Process Automation.- Is Robotic Process Automation Becoming Intelligent? Early Evidence of Influences of Artificial Intelligence on Robotic Process Automation.- Automated Generation of Executable RPA Scripts from User Interface Logs.- Integrating Robotic Process Automation into Business Process Management.- How to Trust a Bot: An RPA User Perspective.- Towards a Taxonomy of Cognitive RPA Components.- Towards an OpenSource Logger for the Analysis of RPA Projects.- Beyond the Hype: RPA Horizon for Robot-Human interaction.- A Framework for Evaluating the Suitability of Process Activities for Robotic Process Automation.- From Robotic Process Automation to Intelligent Process Automation.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the Blockchain and Robotic Process Automation (RPA) Forum which was held as part of the 18th International Conference on Business Process Management, BPM 2020. The conference was planned to take place in Seville, Spain, in September 2020. Due to the COVID-19 pandemic the conference took place virtually. The Blockchain Forum and the RPA Forum have in common that they are centered around an emerging and exciting technology. The blockchain is a sophisticated distributed ledger technology, while RPA software allows for mimicking human, repetitive actions. Each of these have the potential to fundamentally change how business processes are being orchestrated and executed in practice. The BPM community has embraced these technologies as objects of analysis, design, development, and evaluation. The 14 full plus one short paper presented in this volume were carefully reviewed and selected from a total of 28 submissions. .
(source: Nielsen Book Data)

• Smart Products.- Assessment Approaches.- PLM Maturity.- Building Information Modeling (BIM).- Languages and Ontologies.- Product Service Systems.- Future Factory.- Knowledge Creation and Management.- Simulation and Virtual Environments.- Sustainability and Systems Improvement.- Configuration and Engineering Change.- Education studies.- Cyberphysical and Smart Systems.- Design and Integration issues.- LM Processes and Applications.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 12th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2015, held in Doha, Qatar, in October 2015. The 79 revised full papers were carefully reviewed and selected from 130 submissions. The papers are organized in the following topical sections: smart products, assessment approaches, PLM maturity, building information modeling (BIM), languages and ontologies, product service systems, future factory, knowledge creation and management, simulation and virtual environments, sustainability and systems improvement, configuration and engineering change, education studies, cyber-physical and smart systems, design and integration issues, and PLM processes and applications.
(source: Nielsen Book Data)