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This first book focuses on uncertain graph and network optimization. It covers three different main contents: uncertain graph, uncertain programming and uncertain network optimization. It also presents applications of uncertain network optimization in a lot of real problems such as transportation problems, dispatching medical supplies problems and location problems. The book is suitable for researchers, engineers, teachers and students in the field of mathematics, information science, computer science, decision science, management science and engineering, artificial intelligence, industrial engineering, economics and operations research.
Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database and data mining communities. Many classical algorithms such as reachability and shortest path queries become #P-complete and, thus, more expensive over uncertain graphs. Moreover, various complex queries and analytics are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this book, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries and analytics. We emphasize the current challenges and highlight some future research directions.
This book deals with decision making in environments of significant data un certainty, with particular emphasis on operations and production management applications. For such environments, we suggest the use of the robustness ap proach to decision making, which assumes inadequate knowledge of the decision maker about the random state of nature and develops a decision that hedges against the worst contingency that may arise. The main motivating factors for a decision maker to use the robustness approach are: • It does not ignore uncertainty and takes a proactive step in response to the fact that forecasted values of uncertain parameters will not occur in most environments; • It applies to decisions of unique, non-repetitive nature, which are common in many fast and dynamically changing environments; • It accounts for the risk averse nature of decision makers; and • It recognizes that even though decision environments are fraught with data uncertainties, decisions are evaluated ex post with the realized data. For all of the above reasons, robust decisions are dear to the heart of opera tional decision makers. This book takes a giant first step in presenting decision support tools and solution methods for generating robust decisions in a variety of interesting application environments. Robust Discrete Optimization is a comprehensive mathematical programming framework for robust decision making.
This Festschrift volume is published in honor of Günter Haring on the occasion of his emerital celebration and contains invited papers by key researchers in the field of performance evaluation presented at the workshop Performance Evaluation of Computer and Communication Systems - Milestones and Future Challenges, PERFORM 2010, held in Vienna, Austria, in October 2010. Günter Haring has dedicated most of his scientific professional life to performance evaluation and the design of distributed systems, contributing in particular to the field of workload characterization. In addition to his own contributions and leadership in international research projects, he is and has been an excellent mentor of young researchers demonstrated by their own brilliant scientific careers. The 20 thoroughly refereed papers range from visionary to in-depth research papers and are organized in the following topical sections: milestones and evolutions; trends: green ICT and virtual machines; modeling; mobility and mobile networks; communication and computer networks; and load balancing, analysis, and management.
In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering. The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2016, held in Aarhus, Denmark, in August 2016. The 11 revised full papers presented together with one tutorial paper were carefully reviewed and selected from 30 initial submissions. They deal with the following topics: algorithmic aspects of elasticity and scalability for distributed, large-scale data stores (e.g. NoSQL and columnar databases); search and retrieval algorithms for cloud infrastructures; monitoring and analysis of elasticity for virtualized environments; NoSQL, schemaless data modeling, integration; caching and load-balancing; storage structures and indexing for cloud databases; new algorithmic aspects of parallel and distributed computing for cloud applications; scalable machine learning, analytics and data science; high availability, reliability, failover; transactional models and algorithms for cloud databases; query languages and processing programming models; consistency, replication and partitioning CAP, data structures and algorithms for eventually consistent stores.
This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. For this new edition the entire text has been totally rewritten. The chapters on chance theory and uncertainty theory are completely new. Mathematicians, researchers, engineers, designers, and students will find this work a stimulating and useful reference.