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Author: Jeffrey Scott Vitter
Publisher: Now Publishers Inc
ISBN: 1601981066
Category : Computers
Languages : en
Pages : 192
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Book Description
Describes several useful paradigms for the design and implementation of efficient external memory (EM) algorithms and data structures. The problem domains considered include sorting, permuting, FFT, scientific computing, computational geometry, graphs, databases, geographic information systems, and text and string processing.
Author: Jeffrey Scott Vitter
Publisher: Now Publishers Inc
ISBN: 1601981066
Category : Computers
Languages : en
Pages : 192
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Book Description
Describes several useful paradigms for the design and implementation of efficient external memory (EM) algorithms and data structures. The problem domains considered include sorting, permuting, FFT, scientific computing, computational geometry, graphs, databases, geographic information systems, and text and string processing.
Author: James M. Abello
Publisher: American Mathematical Soc.
ISBN: 0821811843
Category : Computers
Languages : en
Pages : 321
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Book Description
The algorithms involve using techniques from computer science and mathematics to solve combinatorial problems whose associated data require the use of a hierarchy of storage devices. The 15 papers discuss such topics as synopsis data structures for massive data sets, maximum clique problems in very large graphs, concrete software libraries, computing on data streams, efficient cross-trees for external memory, efficient schemes for distributing data on parallel memory systems, and external memory techniques for iso-surface extraction in scientific visualization. Annotation copyrighted by Book News, Inc., Portland, OR.
Author: Ulrich Meyer
Publisher: Springer
ISBN: 3540365745
Category : Computers
Languages : en
Pages : 443
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Book Description
Algorithms that have to process large data sets have to take into account that the cost of memory access depends on where the data is stored. Traditional algorithm design is based on the von Neumann model where accesses to memory have uniform cost. Actual machines increasingly deviate from this model: while waiting for memory access, nowadays, microprocessors can in principle execute 1000 additions of registers; for hard disk access this factor can reach six orders of magnitude. The 16 coherent chapters in this monograph-like tutorial book introduce and survey algorithmic techniques used to achieve high performance on memory hierarchies; emphasis is placed on methods interesting from a theoretical as well as important from a practical point of view.
Author: Dzejla Medjedovic
Publisher: Simon and Schuster
ISBN: 1638356564
Category : Computers
Languages : en
Pages : 302
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Book Description
Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting
Author: Pat Morin
Publisher: Athabasca University Press
ISBN: 1927356385
Category : Computers
Languages : en
Pages : 336
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Book Description
Introduction -- Array-based lists -- Linked lists -- Skiplists -- Hash tables -- Binary trees -- Random binary search trees -- Scapegoat trees -- Red-black trees -- Heaps -- Sorting algorithms -- Graphs -- Data structures for integers -- External memory searching.
Author: Gianfranco Bilardi
Publisher: Lecture Notes in Computer Science
ISBN:
Category : Computers
Languages : en
Pages : 538
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Book Description
This book constitutes the refereed proceedings of the 6th Annual European Symposium on Algorithms, ESA'97, held in Venice, Italy, in August 1998. The 40 revised full papers presented together with two invited contributions were carefully reviewed and selected from a total of 131 submissions. The book is divided into sections on data structures, strings and biology, numerical algorithms, geometry, randomized and online algorithms, parallel and distributed algorithms, graph algorithms, and optimization.
Author: James Abello
Publisher: Springer
ISBN: 1461500052
Category : Computers
Languages : en
Pages : 1209
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Book Description
The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive. " The Handbook of Massive Data Sets is comprised of articles writ ten by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, dustering methods, wavelets, op timization, external memory algorithms and data structures, the US national duster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment.
Author: Hong Shen
Publisher: Springer Nature
ISBN: 9811527679
Category : Computers
Languages : en
Pages : 563
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Book Description
This book constitutes the refereed proceedings of the 10th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2019, held in Guangzhou, China, in December 2019. The 39 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 121 submissions. The papers deal with research results and development activities in all aspects of parallel architectures, algorithms and programming techniques.
Author: Jan Vahrenhold
Publisher: Springer
ISBN: 3642020119
Category : Computers
Languages : en
Pages : 302
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Book Description
This book constitutes the refereed proceedings of the 8th International Symposium on Experimental and Efficient Algorithms, SEA 2009, held in Dortmund, Germany, in June 2009. The 23 revised full papers were carefully reviewed and selected from 64 submissions and present current research on experimental evaluation and engineering of algorithms, as well as in various aspects of computational optimization and its applications. Contributions are supported by experimental evaluation, methodological issues in the design and interpretation of experiments, the use of (meta- ) heuristics, or application-driven case studies that deepen the understanding of a problem's complexity.
Author: Michael T. Goodrich
Publisher: John Wiley & Sons
ISBN: 1118771338
Category : Computers
Languages : en
Pages : 736
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Book Description
The design and analysis of efficient data structures has long been recognized as a key component of the Computer Science curriculum. Goodrich, Tomassia and Goldwasser's approach to this classic topic is based on the object-oriented paradigm as the framework of choice for the design of data structures. For each ADT presented in the text, the authors provide an associated Java interface. Concrete data structures realizing the ADTs are provided as Java classes implementing the interfaces. The Java code implementing fundamental data structures in this book is organized in a single Java package, net.datastructures. This package forms a coherent library of data structures and algorithms in Java specifically designed for educational purposes in a way that is complimentary with the Java Collections Framework.
