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Reinforcement Learning for Reconfigurable Intelligent Surfaces: Assisted Wireless Communication Systems (SpringerBriefs in Computer Science)
by Octavia A. Dobre Alice Faisal Ibrahim Al-Nahhal Telex M. NgatchedThis book presents the intersection of two dynamic fields: Reinforcement Learning (RL) and RIS- Assisted Wireless Communications. With an emphasis on both discrete and continuous problems, it introduces a comprehensive overview of RL techniques and their applications in the evolving world of RIS-assisted wireless communications. Chapter 1 introduces the fundamentals of RL and deep RL (DRL), providing a solid foundation for understanding subsequent chapters. It also presents the Q-learning, deep Q-learning, and deep deterministic policy gradient algorithms. Chapter 2 provides a holistic overview of RIS-assisted systems and details several use cases in wireless communications. Then, Chapters 3 and 4 present various applications of the discrete and continuous DRL to RIS-assisted wireless communications. From maximizing the sum-rate to minimizing, the system resources and maximizing the energy efficiency. These chapters showcase the versatility of the DRL algorithms in tackling arange of challenges. This book concludes with Chapter 5, which introduces the challenges and future directions in this field. It explores the particulars of hyperparameter tuning, problem design, and complexity analysis, while also highlighting the potential of hybrid DRL, multi-agent DRL, and transfer learning techniques for advancing wireless communication systems. Optimizing RIS-Assisted Wireless Systems requires powerful algorithms to cope with the dynamic propagation environment. DRL is envisioned as one of the key enabling techniques to exploit the full potential of RIS-Assisted Wireless Communication Systems. It empowers these systems to intelligently adapt to dynamic wireless environments, maximize performance metrics, and adjusts their configurations to accommodate diverse use cases efficiently. This book serves as a valuable resource, shedding light on the potential of DRL to optimize RIS-Assisted Wireless Communication, enabling researchers, engineers, advanced level students in computer science and electrical engineering and enthusiasts to grasp the intricacies of this topic. It offers a comprehensive understanding of the principles, applications, and challenges, making it a reference to recognize the full potential of the RIS technology in modern wireless communication systems.
Reinforcement Learning in the Ridesharing Marketplace (Synthesis Lectures on Learning, Networks, and Algorithms)
by Hongtu Zhu Jieping Ye Zhiwei (Tony) Qin Xiaocheng Tang Qingyang LiThis book provides a comprehensive overview of reinforcement learning for ridesharing applications. The authors first lay out the fundamentals of the ridesharing system architectures and review the basics of reinforcement learning, including the major applicable algorithms. The book describes the research problems associated with the various aspects of a ridesharing system and discusses the existing reinforcement learning approaches for solving them. The authors survey the existing research on each problem, and then examine specific case studies. The book also includes a review of two of methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control.
Reinforcement Learning of Bimanual Robot Skills (Springer Tracts in Advanced Robotics #134)
by Adrià Colomé Carme TorrasThis book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning.
Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
by Leonhard KunczikThis book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution. The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It proposes a novel quantum REINFORCE algorithm that enhances its classical counterpart by Quantum Variational Circuits. The new algorithm is compared to classical algorithms regarding the convergence speed and memory usage on several attacker-defender scenarios with increasing complexity. In addition, to study its applicability on today's NISQ hardware, the algorithm is evaluated on IBM's quantum computers, which is accompanied by an in-depth analysis of the advantages of Quantum Reinforcement Learning.
Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
by Sayon DuttaLeverage the power of the Reinforcement Learning techniques to develop self-learning systems using TensorflowKey Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and moreBook DescriptionReinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions.The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in roboticsWho this book is forIf you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.
Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series #173)
by Richard S. Sutton Andrew G. BartoRichard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series #173)
by Richard S. Sutton Andrew G. BartoThe significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Reinforcement Learning: Aktuelle Ansätze verstehen - mit Beispielen in Java und Greenfoot
by Uwe LorenzIn uralten Spielen wie Schach oder Go können sich die brillantesten Spieler verbessern, indem sie die von einer Maschine produzierten Strategien studieren. Robotische Systeme üben ihre Bewegungen selbst. In Arcade Games erreichen lernfähige Agenten innerhalb weniger Stunden übermenschliches Niveau. Wie funktionieren diese spektakulären Algorithmen des bestärkenden Lernens? Mit gut verständlichen Erklärungen und übersichtlichen Beispielen in Java und Greenfoot können Sie sich die Prinzipien des bestärkenden Lernens aneignen und in eigenen intelligenten Agenten anwenden. Greenfoot (M.Kölling, King’s College London) und das Hamster-Modell (D.Bohles, Universität Oldenburg) sind einfache aber auch mächtige didaktische Werkzeuge, die entwickelt wurden, um Grundkonzepte der Programmierung zu vermitteln. Wir werden Figuren wie den Java-Hamster zu lernfähigen Agenten machen, die eigenständig ihre Umgebung erkunden.
Reinforcement Learning: Aktuelle Ansätze verstehen – mit Beispielen in Java und Greenfoot
by Uwe LorenzIn uralten Spielen wie Schach oder Go können sich die brillantesten Spieler verbessern, indem sie die von einer Maschine produzierten Strategien studieren. Robotische Systeme üben ihre Bewegungen selbst. In Arcade Games erreichen lernfähige Agenten innerhalb weniger Stunden übermenschliches Niveau. Wie funktionieren diese spektakulären Algorithmen des bestärkenden Lernens? Mit gut verständlichen Erklärungen und übersichtlichen Beispielen in Java und Greenfoot können Sie sich die Prinzipien des bestärkenden Lernens aneignen und in eigenen intelligenten Agenten anwenden. Greenfoot (M.Kölling, King’s College London) und das Hamster-Modell (D.Bohles, Universität Oldenburg) sind einfache, aber auch mächtige didaktische Werkzeuge, die entwickelt wurden, um Grundkonzepte der Programmierung zu vermitteln. Wir werden Figuren wie den Java-Hamster zu lernfähigen Agenten machen, die eigenständig ihre Umgebung erkunden. Die zweite Auflage enthält neue Themen wie "Genetische Algorithmen" und "Künstliche Neugier" sowie Korrekturen und Überarbeitungen.
Reinforcement Learning: An Introduction
by Richard S. Sutton Andrew G. BartoReinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
Reinforcement Learning: Industrial Applications Of Intelligent Agents
by Phil WinderReinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself.Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML.Learn what RL is and how the algorithms help solve problemsBecome grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learningDive deep into a range of value and policy gradient methodsApply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learningUnderstand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and moreGet practical examples through the accompanying website
Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning (HIMSS Book Series)
by Paul Cerrato John HalamkaThis book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it’s forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.
Reinventing Higher Education: The Promise of Innovation
by Ben WildavskyThe inspiration for this timely book is the pressing need for fresh ideas and innovations in U.S. higher education. At the heart of the volume is the realization that higher education must evolve in fundamental ways if it is to respond to changing professional, economic, and technological circumstances, and if it is to successfully reach and prepare a vast population of students—traditional and nontraditional alike—for success in the coming decades. This collection of provocative articles by leading scholars, writers, innovators, and university administrators examines the current higher education environment and its chronic resistance to change; the rise of for-profit universities; the potential future role of community colleges in a significantly revised higher education realm; and the emergence of online learning as a means to reshape teaching and learning and to reach new consumers of higher education. Combining trenchant critiques of current conditions with thought-provoking analyses of possible reforms and new directions, Reinventing Higher Education is an ambitious exploration of possible future directions for revitalized American colleges and universities.
Reinventing ITIL® and DevOps with Digital Transformation: Essential Guidance to Accelerate the Process
by Abhinav Krishna KaiserThe second edition of this book has been fully updated to show how the DevOps way of working has continued to adapt to changing technologies. The ITIL processes which were an integral part of the DevOps world have been merged with the DevOps framework, reflecting the current emphasis on product models rather than viewing project and support models separately. This book starts with the basics of digital transformation before exploring how this works in practice: that is, people, processes and technology, and org structures. It delves into value streams that are the basis for ITIL and DevOps, highlighting the differences between the methods of the past and new methodologies needed to ensure products to meet contemporary expectations. This updated edition includes new chapters that discuss digital transformation for business success, introduce the battle tank framework, leading people in the digital world, managing work in a remote working model, and the product-led transformation model. These new chapters provide the guidance necessary to move beyond DevOps into a holistic digital transformation exercise.The ideas, recommendations, and solutions you'll learn over the course of this book can be applied to develop solutions or create proposals for clients, and to deliver seamless services for DevOps projects.What You Will LearnUnderstand digital transformationLeverage the battle tank framework for digital transformationGain insight into the confluence of DevOps and ITILAdapt ITIL processes in DevOps projectsMove organizations from a project to a product-led model Lead teams in a digital worldManage the work of remote teamsWho This Book Is ForIT consultants and IT professionals who are looking for guidance to strategize, plan and implement digital transformation initiatives; design and redesign ITIL processes to adapt to the digital ways of working; moving organizations to product-led business; and leading people and managing work in the digital age.
Reinventing ITIL® in the Age of DevOps: Innovative Techniques To Make Processes Agile And Relevant
by Abhinav Krishna KaiserDelve into the principles of ITIL® and DevOps and examine the similarities and differences. This book re-engineers the ITIL framework to work in DevOps projects without changing its meaning and its original objectives, making it fit for purpose for use in DevOps projects. Reinventing ITIL® in the Age of DevOpsshows you the relevance of ITIL since the emergence of DevOps and puts a unique spin on the ITIL service management framework. Along the way you will see that ITIL is a mature service management framework and years of maturity will be lost if it’s made invalid. The ideas, recommendations, and solutions provided in Reinventing ITIL in the Age of DevOps can be leveraged in order to readily develop solutions or create proposals for clients. The ideas in this book can be further expanded to deliver seamless services to DevOps projects. What You Will LearnDiscover the basics of ITIL and DevOpsCompare ITIL and DevOpsUnderstand the structure of a DevOps organization and adapt the ITIL roles to this structureRe-engineer ITIL for DevOps projectsImplement major processes such as incident management, configuration management, and change management processes in DevOps projectsAutomate activities within processesWho This Book Is For Consultants, business analysts, administrators, and project managers who are looking for more information about Dynamics 365.
Reinventing Ourselves: Contemporary Concepts of Identity in Virtual Worlds
by Anna Peachey Mark ChildsThe proposed book explores the theme of identity, specifically as applied to its role and development in virtual worlds. Following the introduction, it is divided into four sections: identities, avatars and the relationship between them; factors that support the development of identity in virtual worlds; managing multiple identities across different environments and creating an online identity for a physical world purpose.
Reinventing Writing: The 9 Tools That Are Changing Writing, Teaching, and Learning Forever
by Vicki DavisIn this much-anticipated book from acclaimed blogger Vicki Davis (Cool Cat Teacher), you’ll learn the key shifts in writing instruction necessary to move students forward in today’s world. Vicki describes how the elements of traditional writing are being reinvented with cloud-based tools. Instead of paper, note taking, filing cabinets, word processors, and group reports, we now have tools like ePaper, eBooks, social bookmarking, cloud syncing, infographics, and more. Vicki shows you how to select the right tool, set it up quickly, and prevent common mistakes. She also helps you teach digital citizenship and offers exciting ways to build writing communities where students love to learn. Special Features:• Essential questions at the start of each chapter to get you thinking about the big ideas• A chapter on each of the nine essential cloud-based tools--ePaper and eBooks; digital notebooks; social bookmarking; cloud syncing; cloud writing apps; blogging and microblogging; wikis and website builders; online graphic organizers and mind maps; and cartoons and infographics• A wide variety of practical ways to use each tool in the classroom• Alignments to the Common Core State Standards in writing • Level Up Learning--a special section at the end of each chapter to help you review, reflect on, and apply what you’ve learned• Writing tips to help you make the best use of the tools and avoid common pitfalls• A glossary of key terms discussed in the book• Useful appendices, including reproducible material for your classroom No matter what grade level you teach or how much tech experience you have, you will benefit from Vicki’s compelling and practical ideas. As she emphasizes throughout this essential book, teaching with cloud-based tools has never been easier, more convenient, or more important than right now.
Reinventing the Classroom Experience: Learning Anywhere, Anytime
by Nancy SullaLearn how to design versatile learning environments in which instruction is as effective virtually as it is in person. Bestselling author and consultant Nancy Sulla shows how you can reinvent the classroom experience and provide high-quality instruction that works as well at home as it does in school. You will discover how to help students build strong work habits and empower them to take responsibility for their learning; five key types of instructional activities; the power of PBL to increase student engagement and motivation; and five types of synchronous engagement between teachers and students. You will also gain strategies for building social and emotional learning, positioning the teacher as the facilitator of learning and parents as partners, and keeping equity at the forefront. No matter what grade level you teach or whether you are teaching fully in school, remotely, or a combination of both, this essential book will help you understand the key structures and strategies that work so students are positioned to learn anywhere, anytime.
Reinventing the IT Department (Computer Weekly Professional Ser.)
by Terry White'Reinventing the Information Technology Department' is both anecdotal and informal but deals with a subject which is of vital interest to Chief Information Officers and IT Managers, addressing questions such as:* How does the IT department keep pace with business change?* How do we provide stable and responsive IT platforms?* How do we add recognised value to the organisation?* How do I reinvent my department?* How do I get onto the board? It offers an alternative view of the new roles of the in-house IT function and proposes a rethink about IT services within companies, suggesting a self-help approach to redefining/reinventing in-house IT for CIOs.The author explains that new modes of business thinking and operation are essential if a company is to succeed in the near future and in light of this covers topics such as self-organising systems, knowledge management, multi-stakeholder perspectives, and empowerment initiatives in relation to the overall business and in particular the IT function.Each chapter contains implementation templates for the readers to take themselves through the repositioning or reengineering of the IT function and their own departments.
Reinvention of Health Applications with IoT: Challenges and Solutions (Demystifying Technologies for Computational Excellence)
by Dr A. AmbikapathyThis book discusses IoT in healthcare and how it enables interoperability, machine-to-machine communication, information exchange, and data movement. It also covers how healthcare service delivery automates patient care with the help of mobility solutions, new technologies, and next-gen healthcare facilities with challenges faced and suggested solutions prescribed. Reinvention of Health Applications with IoT: Challenges and Solutions presents the latest applications of IoT in healthcare along with challenges and solutions. It looks at a comparison of advanced technologies such as Deep Learning, Machine Learning, and AI and explores the ways they can be applied to sensed data to improve prediction and decision-making in smart health services. It focuses on society 5.0 technologies and illustrates how they can improve society and the transformation of IoT in healthcare facilities to support patient independence. Case studies are included for applications such as smart eyewear, smart jackets, and smart beds. The book will also go into detail on wearable technologies and how they can communicate patient information to doctors in medical emergencies. The target audiences for this edited volume is researchers, practitioners, students, as well as key stakeholders involved in and working on healthcare engineering solutions.
Relating Software Requirements and Architectures
by John Grundy Ivan Mistrík Jon G. Hall Patricia Lago Paris AvgeriouWhy have a book about the relation between requirements and software architecture? Understanding the relation between requirements and architecture is important because the requirements, be they explicit or implicit, represent the function, whereas the architecture determines the form. While changes to a set of requirements may impact on the realization of the architecture, choices made for an architectural solution may impact on requirements, e.g., in terms of revising functional or non-functional requirements that cannot actually be met. Although research in both requirements engineering and software architecture is quite active, it is in their combination that understanding is most needed and actively sought. Presenting the current state of the art is the purpose of this book. The editors have divided the contributions into four parts: Part 1 "Theoretical Underpinnings and Reviews" addresses the issue of requirements change management in architectural design through traceability and reasoning. Part 2 "Tools and Techniques" presents approaches, tools, and techniques for bridging the gap between software requirements and architecture. Part 3 "Industrial Case Studies" then reports industrial experiences, while part 4 on "Emerging Issues" details advanced topics such as synthesizing architecture from requirements or the role of middleware in architecting for non-functional requirements. The final chapter is a conclusions chapter identifying key contributions and outstanding areas for future research and improvement of practice. The book is targeted at academic and industrial researchers in requirements engineering or software architecture. Graduate students specializing in these areas as well as advanced professionals in software development will also benefit from the results and experiences presented in this volume.
Relating Software Requirements and Architectures
by John Grundy Ivan Mistrík Jon G. Hall Patricia Lago Paris AvgeriouWhy have a book about the relation between requirements and software architecture? Understanding the relation between requirements and architecture is important because the requirements, be they explicit or implicit, represent the function, whereas the architecture determines the form. While changes to a set of requirements may impact on the realization of the architecture, choices made for an architectural solution may impact on requirements, e.g., in terms of revising functional or non-functional requirements that cannot actually be met.Although research in both requirements engineering and software architecture is quite active, it is in their combination that understanding is most needed and actively sought. Presenting the current state of the art is the purpose of this book. The editors have divided the contributions into four parts: Part 1 “Theoretical Underpinnings and Reviews” addresses the issue of requirements change management in architectural design through traceability and reasoning. Part 2 “Tools and Techniques” presents approaches, tools, and techniques for bridging the gap between software requirements and architecture. Part 3 “Industrial Case Studies” then reports industrial experiences, while part 4 on “Emerging Issues” details advanced topics such as synthesizing architecture from requirements or the role of middleware in architecting for non-functional requirements. The final chapter is a conclusions chapter identifying key contributions and outstanding areas for future research and improvement of practice.The book is targeted at academic and industrial researchers in requirements engineering or software architecture. Graduate students specializing in these areas as well as advanced professionals in software development will also benefit from the results and experiences presented in this volume.
Relational Calculus for Actionable Knowledge (Information Fusion and Data Science)
by Éloi Bossé Michel BarèsThis book focuses on one of the major challenges of the newly created scientific domain known as data science: turning data into actionable knowledge in order to exploit increasing data volumes and deal with their inherent complexity. Actionable knowledge has been qualitatively and intensively studied in management, business, and the social sciences but in computer science and engineering, its connection has only recently been established to data mining and its evolution, ‘Knowledge Discovery and Data Mining’ (KDD). Data mining seeks to extract interesting patterns from data, but, until now, the patterns discovered from data have not always been ‘actionable’ for decision-makers in Socio-Technical Organizations (STO). With the evolution of the Internet and connectivity, STOs have evolved into Cyber-Physical and Social Systems (CPSS) that are known to describe our world today. In such complex and dynamic environments, the conventional KDD process is insufficient, and additional processes are required to transform complex data into actionable knowledge. Readers are presented with advanced knowledge concepts and the analytics and information fusion (AIF) processes aimed at delivering actionable knowledge. The authors provide an understanding of the concept of ‘relation’ and its exploitation, relational calculus, as well as the formalization of specific dimensions of knowledge that achieve a semantic growth along the AIF processes. This book serves as an important technical presentation of relational calculus and its application to processing chains in order to generate actionable knowledge. It is ideal for graduate students, researchers, or industry professionals interested in decision science and knowledge engineering.
Relational Data Clustering: Models, Algorithms, and Applications
by Philip S. Yu Zhongfei Zhang Bo LongA culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems.After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering:Clustering on bi-type heterogeneous relational dataMulti-type heterogeneous relational data Homogeneous relational data clusteringClustering on the most general case of relational dataIndividual relational clustering frameworkRecent research on evolutionary clusteringThis book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Relational Database Programming
by Stefan ArdeleanuLearn the best way of writing code to run inside a relational database. This book shows how a holistic and set-oriented approach to database programming can far exceed the performance of the row-by-row model that is too often used by developers who haven’t been shown a better way. <P><P> Two styles of programming are encountered in the database world. Classical programming as taught in many universities leads to an atomic, row-oriented, and procedural style inspired by the structured models of programming. In short, many application developers write in the relational database exactly like in the user interface. The other style of programming is holistic, data set oriented, and coded mainly in SQL. This is the style of the database developer.<P> The set based and holistic style of development is not promoted enough in universities, and many application developers are not fully aware of it. There are many performance issues all over the world in relational databases due to the use of the atomic and inappropriate style of programming. This book compares the two styles, and promotes the holistic style of development as the most suitable one. Examples are given to demonstrate the superiority of a set-based and holistic approach.<P> * Compares the two styles of development<P> * Shows the performance advantages of set-based development<P> * Solves example problems using both approaches<P> Who This Book Is For<P> Two Styles of Database Development is aimed at application developers willing to adapt their programming styles in return for better-performing applications. It’s for students and new developers wanting to position themselves as having database expertise and build a reputation for developing highly-performant database applications.