Using Additional Information in Streaming Algorithms

Author: Raffael Buff
Publisher: diplom.de
ISBN: 3961160422
Format: PDF
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Streaming problems are algorithmic problems that are mainly characterized by their massive input streams. Because of these data streams, the algorithms for these problems are forced to be space-efficient, as the input stream length generally exceeds the available storage. In this thesis, the two streaming problems most frequent item and number of distinct items are studied in detail relating to their algorithmic complexities, and it is compared whether the verification of solution hypotheses has lower algorithmic complexity than computing a solution from the data stream. For this analysis, we introduce some concepts to prove space complexity lower bounds for an approximative setting and for hypothesis verification. For the most frequent item problem which consists in identifying the item which has the highest occurrence within the data stream, we can prove a linear space complexity lower bound for the deterministic and probabilistic setting. This implies that, in practice, this streaming problem cannot be solved in a satisfactory way since every algorithm has to exceed any reasonable storage limit. For some settings, the upper and lower bounds are almost tight, which implies that we have designed an almost optimal algorithm. Even for small approximation ratios, we can prove a linear lower bound, but not for larger ones. Nevertheless, we are not able to design an algorithm that solves the most frequent item problem space-efficiently for large approximation ratios. Furthermore, if we want to verify whether a hypothesis of the highest frequency count is true or not, we get exactly the same space complexity lower bounds, which leads to the conclusion that we are likely not able to profit from a stated hypothesis. The number of distinct items problem counts all different elements of the input stream. If we want to solve this problem exactly (in a deterministic or probabilistic setting) or approximately with a deterministic algorithm, we require once again linear storage size which is tight to the upper bound. However, for the approximative and probabilistic setting, we can enhance an already known space-efficient algorithm such that it is usable for arbitrarily small approximation ratios and arbitrarily good success probabilities. The hypothesis verification leads once again to the same lower bounds. However, there are some streaming problems that are able to profit from additional information such as hypotheses, as e.g., the median problem.

Delaunay Mesh Generation

Author: Siu-Wing Cheng
Publisher: CRC Press
ISBN: 1584887311
Format: PDF, ePub, Mobi
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Written by authors at the forefront of modern algorithms research, Delaunay Mesh Generation demonstrates the power and versatility of Delaunay meshers in tackling complex geometric domains ranging from polyhedra with internal boundaries to piecewise smooth surfaces. Covering both volume and surface meshes, the authors fully explain how and why these meshing algorithms work. The book is one of the first to integrate a vast amount of cutting-edge material on Delaunay triangulations. It begins with introducing the problem of mesh generation and describing algorithms for constructing Delaunay triangulations. The authors then present algorithms for generating high-quality meshes in polygonal and polyhedral domains. They also illustrate how to use restricted Delaunay triangulations to extend the algorithms to surfaces with ridges and patches and volumes with smooth surfaces. For researchers and graduate students, the book offers a rigorous theoretical analysis of mesh generation methods. It provides the necessary mathematical foundations and core theoretical results upon which researchers can build even better algorithms in the future. For engineers, the book shows how the algorithms work well in practice. It explains how to effectively implement them in the design and programming of mesh generation software.

Methods in Algorithmic Analysis

Author: Vladimir A. Dobrushkin
Publisher: CRC Press
ISBN: 9781420068306
Format: PDF, ePub
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Explores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer Science A flexible, interactive teaching format enhanced by a large selection of examples and exercises Developed from the author’s own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science. After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes’ theorem, and Chebyshev inequality. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications. The author then describes the powerful tool of generating functions, which is demonstrated in enumeration problems, such as probabilistic algorithms, compositions and partitions of integers, and shuffling. He also discusses the symbolic method, the principle of inclusion and exclusion, and its applications. The book goes on to show how strings can be manipulated and counted, how the finite state machine and Markov chains can help solve probabilistic and combinatorial problems, how to derive asymptotic results, and how convergence and singularities play leading roles in deducing asymptotic information from generating functions. The final chapter presents the definitions and properties of the mathematical infrastructure needed to accommodate generating functions. Accompanied by more than 1,000 examples and exercises, this comprehensive, classroom-tested text develops students’ understanding of the mathematical methodology behind the analysis of algorithms. It emphasizes the important relation between continuous (classical) mathematics and discrete mathematics, which is the basis of computer science.

Handbook of Algorithms for Wireless Networking and Mobile Computing

Author: Azzedine Boukerche
Publisher: CRC Press
ISBN: 1420035096
Format: PDF, ePub, Mobi
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Most of the available literature in wireless networking and mobile computing concentrates on the physical aspect of the subject, such as spectrum management and cell re-use. In most cases, a description of fundamental distributed algorithms that support mobile hosts in a wireless environment is either not included or is only briefly discussed. Handbook of Algorithms for Wireless Networking and Mobile Computing focuses on several aspects of mobile computing, particularly algorithmic methods and distributed computing with mobile communications capability. This volume provides the topics that are crucial for building the foundation for the design and construction of future generations of mobile and wireless networks, including cellular, wireless ad hoc, sensor, and ubiquitous networks. Following an analysis of fundamental algorithms and protocols, the book offers a basic overview of wireless technologies and networks and a discussion of the convergence of communication and computation. Other topics include issues related to mobility, with a focus on the creation of techniques that control associated uncertainties; aspects of QoS provisioning in wireless networks; a comparison of numerous wireless TCP proposals; a review of fundamental algorithms for Bluetooth wireless personal area networks (WPANs); and investigations of future voice and video access networks; and a review of potential applications of pervasive computing and mobile e-commerce.

Handbook of Data Structures and Applications

Author: Dinesh P. Mehta
Publisher: CRC Press
ISBN: 9781420035179
Format: PDF, ePub, Docs
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Although there are many advanced and specialized texts and handbooks on algorithms, until now there was no book that focused exclusively on the wide variety of data structures that have been reported in the literature. The Handbook of Data Structures and Applications responds to the needs of students, professionals, and researchers who need a mainstream reference on data structures by providing a comprehensive survey of data structures of various types. Divided into seven parts, the text begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. The Handbook is invaluable in suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.

Handbook of Approximation Algorithms and Metaheuristics

Author: Teofilo F. Gonzalez
Publisher: CRC Press
ISBN: 9781420010749
Format: PDF, ePub, Mobi
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Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical applications. It is the first book to comprehensively study both approximation algorithms and metaheuristics. Starting with basic approaches, the handbook presents the methodologies to design and analyze efficient approximation algorithms for a large class of problems, and to establish inapproximability results for another class of problems. It also discusses local search, neural networks, and metaheuristics, as well as multiobjective problems, sensitivity analysis, and stability. After laying this foundation, the book applies the methodologies to classical problems in combinatorial optimization, computational geometry, and graph problems. In addition, it explores large-scale and emerging applications in networks, bioinformatics, VLSI, game theory, and data analysis. Undoubtedly sparking further developments in the field, this handbook provides the essential techniques to apply approximation algorithms and metaheuristics to a wide range of problems in computer science, operations research, computer engineering, and economics. Armed with this information, researchers can design and analyze efficient algorithms to generate near-optimal solutions for a wide range of computational intractable problems.

Semantic Web

Author: Pascal Hitzler
Publisher: Springer-Verlag
ISBN: 3540339949
Format: PDF
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Das Buch Semantic Web – Grundlagen vermittelt als erstes deutschsprachiges Lehrbuch die Grundlagen des Semantic Web in verständlicher Weise. Es ermöglicht einen einfachen und zügigen Einstieg in Methoden und Technologien des Semantic Web und kann z.B. als solide Grundlage für die Vorbereitung und Durchführung von Vorlesungen genutzt werden. Die Autoren trennen dabei sauber zwischen einer intuitiven Hinführung zur Verwendung semantischer Technologien in der Praxis einerseits, und der Erklärung formaler und theoretischer Hintergründe andererseits. Nur für letzteres werden Grundkenntnisse in Logik vorausgesetzt, die sich bei Bedarf jedoch durch zusätzliche Lektüre und mit Hilfe eines entsprechenden Kapitels im Anhang aneignen lassen. Das Lehrbuch richtet sich primär an Studenten mit Grundkenntnissen in Informatik sowie an interessierte Praktiker welche sich im Bereich Semantic Web fortbilden möchten. Aus den Rezensionen: "... RDF, RDF-S und OWL. Diese Sprachen ... werden von den Autoren dargestellt. Bei der Darstellung ... fallen sie selten zu schwierigen Fachslang, sondern liefern eine gut nachvollziehbare Schilderung mit einfachen Beispielen, auch Übungsaufgaben runden die Kapitel ab. ... Semantic Web ist ein einfach geschriebenes und anschauliches Buch, das In die Grundkonzepte der Semantic-Web-Techniken einführt. Wer sich schnell in RDF, RDF-S und Co. einarbeiten muss und etwas Vorbildung in Logik und Algebra mitbringt, der trifft mit diesem Lehrbuch sicherlich eine gute Wahl ..." (http://www.literaturnetz.com/content/view/8742/44/)

Handbook of Bioinspired Algorithms and Applications

Author: Stephan Olariu
Publisher: CRC Press
ISBN: 1420035061
Format: PDF, ePub, Mobi
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The mystique of biologically inspired (or bioinspired) paradigms is their ability to describe and solve complex relationships from intrinsically very simple initial conditions and with little or no knowledge of the search space. Edited by two prominent, well-respected researchers, the Handbook of Bioinspired Algorithms and Applications reveals the connections between bioinspired techniques and the development of solutions to problems that arise in diverse problem domains. A repository of the theory and fundamentals as well as a manual for practical implementation, this authoritative handbook provides broad coverage in a single source along with numerous references to the available literature for more in-depth information. The book's two sections serve to balance coverage of theory and practical applications. The first section explains the fundamentals of techniques, such as evolutionary algorithms, swarm intelligence, cellular automata, and others. Detailed examples and case studies in the second section illustrate how to apply the theory in actually developing solutions to a particular problem based on a bioinspired technique. Emphasizing the importance of understanding and harnessing the robust capabilities of bioinspired techniques for solving computationally intractable optimizations and decision-making applications, the Handbook of Bioinspired Algorithms and Applications is an absolute must-read for anyone who is serious about advancing the next generation of computing.

Handbook of Computational Molecular Biology

Author: Srinivas Aluru
Publisher: CRC Press
ISBN: 9781420036275
Format: PDF, ePub, Docs
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The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology offers comprehensive, systematic coverage of the various techniques and methodologies currently available. Accomplished researcher Srinivas Aluru leads a team of experts from around the world to produce this groundbreaking, authoritative reference. With discussions ranging from fundamental concepts to practical applications, this book details the algorithms necessary to solve novel problems and manage the massive amounts of data housed in biological databases throughout the world. Divided into eight sections for convenient searching, the handbook covers methods and algorithms for sequence alignment, string data structures, sequence assembly and clustering, genome-scale computational methods in comparative genomics, evolutionary and phylogenetic trees, microarrays and gene expression analysis, computational methods in structural biology, and bioinformatics databases and data mining. The Handbook of Computational Molecular Biology is the first resource to integrate coverage of the broad spectrum of topics in computational biology and bioinformatics. It supplies a quick-reference guide for easy implementation and provides a strong foundation for future discoveries in the field.