Preface 1. The approximation problem and existence of best approximations 2. The uniqueness of best approximations 3. Approximation operators and some approximating functions 4. Polynomial interpolation 5. Divided differences 6. The uniform convergence of polynomial approximations 7. The theory of minimax approximation 8. The exchange algorithm 9. The convergence of the exchange algorithm 10. Rational approximation by the exchange algorithm 11. Least squares approximation 12. Properties of orthogonal polynomials 13. Approximation of periodic functions 14. The theory of best L1 approximation 15. An example of L1 approximation and the discrete case 16. The order of convergence of polynomial approximations 17. The uniform boundedness theorem 18. Interpolation by piecewise polynomials 19. B-splines 20. Convergence properties of spline approximations 21. Knot positions and the calculation of spline approximations 22. The Peano kernel theorem 23. Natural and perfect splines 24. Optimal interpolation Appendices Index.

Approximation theory and methods, by M. J. D. Powell. Pp 339. £25 (hardcover), £8·50 (paperback). 1981. ISBN 0-521-22472-1/29514-9 (Cambridge University Press)

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

Next Generation Cloud Computing: New Trends and Research Directions

Benchmarking is an essential aspect of any database management system (DBMS) effort. Despite several recent advancements, such as pre-configured cloud database images and database-as-a-service (DBaaS) offerings, the deployment of a comprehensive testing platform with a diverse set of datasets and workloads is still far from being trivial. In many cases, researchers and developers are limited to a small number of workloads to evaluate the performance characteristics of their work. This is due to the lack of a universal benchmarking infrastructure, and to the difficulty of gaining access to real data and workloads. This results in lots of unnecessary engineering efforts and makes the performance evaluation results difficult to compare. To remedy these problems, we present OLTP-Bench, an extensible "batteries included" DBMS benchmarking testbed. The key contributions of OLTP-Bench are its ease of use and extensibility, support for tight control of transaction mixtures, request rates, and access distributions over time, as well as the ability to support all major DBMSs and DBaaS platforms. Moreover, it is bundled with fifteen workloads that all differ in complexity and system demands, including four synthetic workloads, eight workloads from popular benchmarks, and three workloads that are derived from real-world applications. We demonstrate through a comprehensive set of experiments conducted on popular DBMS and DBaaS offerings the different features provided by OLTP-Bench and the effectiveness of our testbed in characterizing the performance of database services.

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OLTP-Bench: an extensible testbed for benchmarking relational databases

The Internet of Things paradigm envisions the pervasive interconnection and cooperation of smart things over the current and future Internet infrastructure. The Internet of Things is, thus, the evolution of the Internet to cover the real world, enabling many new services that will improve people's everyday lives, spawn new businesses, and make buildings, cities, and transport smarter. Smart things allow indeed for ubiquitous data collection or tracking, but these useful features are also examples of privacy threats that are already now limiting the success of the Internet of Things vision when not implemented correctly. These threats involve new challenges such as the pervasive privacy-aware management of personal data or methods to control or avoid ubiquitous tracking and profiling. This paper analyzes the privacy issues in the Internet of Things in detail. To this end, we first discuss the evolving features and trends in the Internet of Things with the goal of scrutinizing their privacy implications. Second, we classify and examine privacy threats in this new setting, pointing out the challenges that need to be overcome to ensure that the Internet of Things becomes a reality. Copyright © 2013 John Wiley & Sons, Ltd.

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Privacy in the Internet of Things: threats and challenges

Big data applications in operations/supply-chain management

From Active Data Management to Event-Based Systems and More

Applications with Service-oriented Enterprise Architectures in the Cloud are emerging and will shape future trends in technology and communication. The development of such applications integrates Enterprise Architecture and Management with Architectures for Services & Cloud Computing, Web Services, Semantics and Knowledge-based Systems, Big Data Management, among other Architecture Frameworks and Software Engineering Methods. In the present work in progress research, we explore Service-oriented Enterprise Architectures and application systems in the context of Big Data applications in cloud settings. Using a Big Data scenario, we investigate the integration of Services and Cloud Computing architectures with new capabilities of Enterprise Architectures and Management. The underlying architecture reference model can be used to support semantic analysis and program comprehension of service-oriented Big Data Applications. Enterprise Services Computing is the current trend for powerful large-scale information systems, which increasingly converge with Cloud Computing environments. In this paper we combine architectures for services with cloud computing. We propose a new integration model for service-oriented Enterprise Architectures on basis of ESARC - Enterprise Services Architecture Reference Cube, which is our previous developed service-oriented enterprise architecture classification framework, with MFESA - Method Framework for Engineering System Architectures - for the design of service-oriented enterprise architectures, and the systematic development, diagnostics and optimization of architecture artifacts of service-oriented cloud-based enterprise systems for Big Data applications.

Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud

New concepts and technologies are being introduced continuously for application development in the World-Wide Web. Selecting the right implementation strategies and tools when building a Web application has become a tedious task, requiring in-depth knowledge and significant experience from both software developers and software managers. The mission of this book is to guide the reader through the opaque jungle of Web technologies. Based on their long industrial and academic experience, Stefan Jablonski and his coauthors provide a framework architecture for Web applications which helps choose the best strategy for a given project. The authors classify common technologies and standards like .NET, CORBA, J2EE, DCOM, WSDL and many more with respect to platform, architectural layer, and application package, and guide the reader through a three-phase development process consisting of preparation, design, and technology selection steps. The whole approach is exemplified using a real-world case: the architectural design of an order-entry management system.

Guide to Web Application and Platform Architectures

The architecture and algorithms of database systems have been built around the properties of existing hardware technologies. Many such elementary design assumptions are 20--30 years old. Over the last five years we witness multiple new I/O technologies (e.g. Flash SSDs, NV-Memories) that have the potential of changing these assumptions. Some of the key technological differences to traditional spinning disk storage are: (i) asymmetric read/write performance; (ii) low latencies; (iii) fast random reads; (iv) endurance issues.Cost functions used by traditional database query optimizers are directly influenced by these properties. Most cost functions estimate the cost of algorithms based on metrics such as sequential and random I/O costs besides CPU and memory consumption. These do not account for asymmetry or high random read and inferior random write performance, which represents a significant mismatch.In the present paper we show a new asymmetry-aware cost model for Flash SSDs with adapted cost functions for algorithms such as external sort, hash-join, sequential scan, index scan, etc. It has been implemented in PostgreSQL and tested with TPC-H. Additionally we describe a tool that automatically finds good settings for the base coefficients of cost models. After tuning the configuration of both the original and the asymmetry-aware cost model with that tool, the optimizer with the asymmetry-aware cost model selects faster execution plans for 14 out of the 22 TPC-H queries (the rest being the same or negligibly worse). We achieve an overall performance improvement of 48% on SSD.

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Making cost-based query optimization asymmetry-aware

The concept of Cloud Computing is by now at the peak of public attention and adoption Driven by several economic and technological enablers, Cloud Computing is going to change the way we have to design, maintain and optimise large-scale data-intensive software systems in the future Moving large-scale, data-intensive systems into the Cloud may not always be possible, but would solve many of today's typical problems In this paper we focus on the opportunities and restrictions of current Cloud solutions regarding the data model of such software systems We identify the technological issues coming along with this new paradigm and discuss the requirements to be met by Cloud solutions in order to provide a meaningful alternative to on-premise configurations

Ilia Petrov

Papers

From Active Data Management to Event-Based Systems and More

Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud

Guide to Web Application and Platform Architectures

Making cost-based query optimization asymmetry-aware

Aspects of data-intensive cloud computing