This book is a valuable resource for the increasing body of researchers and practitioners in the field of geospatial technologies. Written by leading researchers and experts it is designed in such a way that technical achievements and challenges of geospatial computing applications are followed by various applications developed for society. As such, they serve as a bridge between technologists and solution developers, which is critical in the context of developing countries. There have been significant advances in geospatial technologies in India in the last decade, including advances in spatial data infrastructures, geocomputation and spatial databases, and innovative applications in natural resource development. Ranging from LIDAR standards, to data integration using ontologies, and mobile computing, such progress enhances the utility of the technology for both urban and rural development. This book discusses these achievements and considers the way forward.

Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications."

Sunanda Sen offers an analysis of the ongoing malaise in the world economy, which include the financial and real instability as well as economic recession and lack of development. Rejecting the explanations advanced by the orthodoxy, she deplores the retrograde steps in the interest of high finance. This calls for a change in policies, away from the contractionary monetarist devices and in the direction of demand expansion which will prove mutually beneficial for both advanced and developing countries.

Sunanda Sen offers an analysis of the ongoing malaise in the world economy, which include the financial and real instability as well as economic recession and lack of development. Rejecting the explanations advanced by the orthodoxy, she deplores the retrograde steps in the interest of high finance. This calls for a change in policies, away from the contractionary monetarist devices and in the direction of demand expansion which will prove mutually beneficial for both advanced and developing countries.

This book summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. Unlike traditional deterministic algorithms, SD combines sampling approaches from the statistical literature with traditional mathematical programming constructs (e.g. decomposition, cutting planes etc.). This marriage of two highly computationally oriented disciplines leads to a line of work that is most definitely driven by computational considerations. Furthermore, the use of sampled data in SD makes it extremely flexible in its ability to accommodate various representations of uncertainty, including situations in which outcomes/scenarios can only be generated by an algorithm/simulation. The authors report computational results with some of the largest stochastic programs arising in applications. These results (mathematical as well as computational) are the tip of the iceberg'. Further research will uncover extensions of SD to a wider class of problems. Audience: Researchers in mathematical optimization, including those working in telecommunications, electric power generation, transportation planning, airlines and production systems. Also suitable as a text for an advanced course in stochastic optimization.

A comprehensive text addressing the high demand for network, cloud, and content services through cutting-edge research on data pricing and business strategies Smart Data Pricing tackles the timely issue of surging demand for network, cloud, and content services and corresponding innovations in pricing these services to benefit consumers, operators, and content providers. The pricing of data traffic and other services is central to the core challenges of network monetization, growth sustainability, and bridging the digital divide. In this book, experts from both academia and industry discuss all aspects of smart data pricing research and development, including economic analyses, system development, user behavior evaluation, and business strategies. Smart Data Pricing: Presents the analysis of leading researchers from industry and academia surrounding the pricing of network services and content. Discusses current trends in mobile and wired data usage and their economic implications for content providers, network operators, end users, government regulators, and other players in the Internet ecosystem. Includes new concepts and background technical knowledge that will help researchers and managers effectively monetize their networks and improve user quality-of-experience. Provides cutting-edge research on business strategies and initiatives through a diverse collection of perspectives. Combines academic and industry expertise from multiple disciplines and business organizations. The ideas and background of the technologies and economic principles discussed within these chapters are of real value to practitioners, researchers, and managers in identifying trends and deploying new pricing and network management technologies, and will help support managers in identifying new business directions and innovating solutions to challenging business problems.