This book constitutes the refereed proceedings of the 19th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2017, held in Boston, MA, USA, in November 2017. The 29 revised full papers presented together with 8 revised short papers were carefully reviewed and selected from 68 initial submissions. This year the Symposium was organized into three tracks reflecting major trends related to self-* systems: Stabilizing Systems: Theory and Practice: Distributed Computing and Communication Networks; and Computer Security and Information Privacy.

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.

This book reviews the concept of Software-Defined Networking (SDN) by studying the SDN architecture. It provides a detailed analysis of state-of-the-art distributed SDN controller platforms by assessing their advantages and drawbacks and classifying them in novel ways according to various criteria. Additionally, a thorough examination of the major challenges of existing distributed SDN controllers is provided along with insights into emerging and future trends in that area. Decentralization challenges in large-scale networks are tackled using three novel approaches, applied to the SDN control plane presented in the book. The first approach addresses the SDN controller placement optimization problem in large-scale IoT-like networks by proposing novel scalability and reliability aware controller placement strategies. The second and third approaches tackle the knowledge sharing problem between the distributed controllers by suggesting adaptive multilevel consistency models following the concept of continuous Quorum-based consistency. These approaches have been validated using different SDN applications, developed from real-world SDN controllers.

Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age.

This book presents the peer-reviewed proceedings of the 4th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2019), held in Cairo, Egypt, on March 28–30, 2019, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover the latest research on machine learning, deep learning, biomedical engineering, control and chaotic systems, text mining, summarization and language identification, machine learning in image processing, renewable energy, cyber security, and intelligence swarms and optimization.

The proceedings of the July 2004 conference consists of 66 papers presenting recent research on peer-to-peer networks, routing in optical networks, caching and scheduling, parallel algorithms, grid and distributed systems, wireless sensor networks, performance evaluation, and load balancing. Three i

Designing distributed computing systems is a complex process requiring a solid understanding of the design problems and the theoretical and practical aspects of their solutions. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource. Additional resources are available online at www.cambridge.org/9780521876346.