In IoT scenarios, ways in which large-scale and cross-domain service systems can be established are still unclear, and no systematic or in-depth theories and methods have yet been found. An effective, formal foundation to IoT application designs could serve as a knowledge base for a variety of virtual world applications. Integrating and Streamlining Event-Driven IoT Services discusses how to observe isolated services running by different observation sources, how to fuse different observations to deal with observation conflict and incompleteness, and how to deal with adversaries and physical system features for real-time property enforcement over the fused knowledge. Overall, presenting an exploration of systematic theories and methods for the design of IoT services based on the principles of streamlining and integration, this book features research on topics such as CEP service, virtual machine technologies, and hybrid EPC. It is ideally designed for engineers, researchers, and university students seeking coverage on applications for smart cities, smart grids, and Industry 4.0.

In today’s market, emerging technologies are continually assisting in common workplace practices as companies and organizations search for innovative ways to solve modern issues that arise. Prevalent applications including internet of things, big data, and cloud computing all have noteworthy benefits, but issues remain when separately integrating them into the professional practices. Significant research is needed on converging these systems and leveraging each of their advantages in order to find solutions to real-time problems that still exist. Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing is a pivotal reference source that provides vital research on the relation between these technologies and the impact they collectively have in solving real-world challenges. While highlighting topics such as cloud-based analytics, intelligent algorithms, and information security, this publication explores current issues that remain when attempting to implement these systems as well as the specific applications IoT, big data, and cloud computing have in various professional sectors. This book is ideally designed for academicians, researchers, developers, computer scientists, IT professionals, practitioners, scholars, students, and engineers seeking research on the integration of emerging technologies to solve modern societal issues.

Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings. Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers’ points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.

For the past couple of years, network automation techniques that include software-defined networking (SDN) and dynamic resource allocation schemes have been the subject of a significant research and development effort. Likewise, network functions virtualization (NFV) and the foreseeable usage of a set of artificial intelligence techniques to facilitate the processing of customers’ requirements and the subsequent design, delivery, and operation of the corresponding services are very likely to dramatically distort the conception and the management of networking infrastructures. Some of these techniques are being specified within standards developing organizations while others remain perceived as a “buzz” without any concrete deployment plans disclosed by service providers. An in-depth understanding and analysis of these approaches should be conducted to help internet players in making appropriate design choices that would meet their requirements as well as their customers. This is an important area of research as these new developments and approaches will inevitably reshape the internet and the future of technology. Design Innovation and Network Architecture for the Future Internet sheds light on the foreseeable yet dramatic evolution of internet design principles and offers a comprehensive overview on the recent advances in networking techniques that are likely to shape the future internet. The chapters provide a rigorous in-depth analysis of the promises, pitfalls, and other challenges raised by these initiatives, while avoiding any speculation on their expected outcomes and technical benefits. This book covers essential topics such as content delivery networks, network functions virtualization, security, cloud computing, automation, and more. This book will be useful for network engineers, software designers, computer networking professionals, practitioners, researchers, academicians, and students looking for a comprehensive research book on the latest advancements in internet design principles and networking techniques.

Diversity in user queries makes it challenging for search engines to effectively return a set of relevant results. Both user intentions to search the web and types of queries are vastly varied; consequently, horizontal and vertical search engines are developed to answer user queries more efficiently. However, these search engines present a variety of problems in web searching. Result Page Generation for Web Searching: Emerging Research and Opportunities is an essential reference publication that focuses on taking advantages from text and web mining in order to address the issues of recommendation and visualization in web searching. Highlighting a wide range of topics such as navigational searching, resource identification, and ambiguous queries, this book is ideally designed for computer engineers, web designers, programmers, academicians, researchers, and students.

TAGLINE Unleash the Power of AWS Serverless Services for Scalable, Resilient, and Reactive Architectures KEY FEATURES ● Master the art of leveraging AWS serverless services to build robust event-driven systems. ● Gain expertise in implementing advanced event-driven patterns in AWS. ● Develop advanced skills in production-ready practices for testing, monitoring, and optimizing event-driven microservices in AWS. DESCRIPTION In the book Mastering Event-Driven Microservices in AWS, author Lefteris Karageorgiou takes you on a comprehensive journey through the world of event-driven architectures and microservices. This practical guide equips you with the knowledge and skills to design, build, and operate resilient, scalable, and fault-tolerant systems using AWS serverless services. Through concrete examples and code samples, you'll learn how to construct real-world event-driven microservices architectures, such as point-to-point messaging, pub/sub messaging, event streaming, and advanced architectures like event sourcing, CQRS, circuit breakers, and sagas. Leveraging AWS services like AWS Lambda, Amazon API Gateway, Amazon EventBridge, Amazon SQS, Amazon SNS, Amazon SQS, AWS Step Functions, and Amazon Kinesis, you'll gain hands-on experience in building robust event-driven applications. The book goes beyond just theory and delves into production-ready practices for testing, monitoring, troubleshooting, and optimizing your event-driven microservices. By the end of this comprehensive book, you'll have the confidence and expertise to design, build, and run mission-critical event-driven microservices in AWS, empowering you to tackle complex distributed systems challenges with ease. Whether you're an experienced developer or a team looking to stay ahead of the curve, Mastering Event-Driven Microservices in AWS is an essential resource that will equip you with the tools and knowledge necessary to harness the power of event-driven microservices in the AWS ecosystem. WHAT WILL YOU LEARN ● Design and implement event-driven microservices on AWS seamlessly. ● Leverage AWS serverless services more effectively. ● Build robust, scalable, and fault-tolerant event-driven applications on AWS. ● Implement advanced event-driven patterns on AWS. ● Monitor and troubleshoot event-driven microservices on AWS effectively. ● Secure and optimize event-driven microservices for production workloads on AWS. WHO IS THIS BOOK FOR? This book is an invaluable resource for developers, architects, and engineers who want to build scalable and efficient applications on the AWS platform using event-driven microservices architecture. It is tailored for professionals with prior experience in cloud computing and microservices development, providing them with the necessary knowledge and skills to leverage AWS serverless services effectively for designing and implementing event-driven microservices. TABLE OF CONTENTS 1. Introduction to Event-Driven Microservices 2. Designing Event-Driven Microservices in AWS 3. Messaging with Amazon SQS and Amazon SNS 4. Choreography with Amazon EventBridge 5. Orchestration with AWS Step Functions 6. Event Streaming with Amazon Kinesis 7. Testing Event-Driven Systems 8. Monitoring and Troubleshooting 9. Optimizations and Best Practices for Production 10. Real-World Use Cases on AWS Index

"Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.