Algorithms And Data Structures For External Memory

Author : Jeffrey Scott Vitter
Publisher : Now Publishers Inc
ISBN 13 : 1601981066
Total Pages : 192 pages
Book Rating : 4.66/5 ( download)

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Download or read book Algorithms and Data Structures for External Memory written by Jeffrey Scott Vitter and published by Now Publishers Inc. This book was released on 2008 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Pat Morin
Publisher : Athabasca University Press
ISBN 13 : 1927356385
Total Pages : 336 pages
Book Rating : 4.88/5 ( download)

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Download or read book Open Data Structures written by Pat Morin and published by Athabasca University Press. This book was released on 2013 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Dzejla Medjedovic
Publisher : Simon and Schuster
ISBN 13 : 1638356564
Total Pages : 302 pages
Book Rating : 4.61/5 ( download)

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Download or read book Algorithms and Data Structures for Massive Datasets written by Dzejla Medjedovic and published by Simon and Schuster. This book was released on 2022-08-16 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Ulrich Meyer
Publisher : Springer
ISBN 13 : 3540365745
Total Pages : 429 pages
Book Rating : 4.47/5 ( download)

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Download or read book Algorithms for Memory Hierarchies written by Ulrich Meyer and published by Springer. This book was released on 2003-07-01 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Frank Dehne
Publisher : Springer
ISBN 13 : 3540484477
Total Pages : 381 pages
Book Rating : 4.79/5 ( download)

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Download or read book Algorithms and Data Structures written by Frank Dehne and published by Springer. This book was released on 2003-07-31 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume were presented at the Sixth Workshop on Algorithms and Data Structures (WADS '99). The workshop took place August 11 - 14, 1999, in Vancouver, Canada. The workshop alternates with the Scandinavian Workshop on Algorithms Theory (SWAT), continuing the tradition of SWAT and WADS starting with SWAT'88 and WADS'89. In response to the program committee's call for papers, 71 papers were submitted. From these submissions, the program committee selected 32 papers for presentation at the workshop. In addition to these submitted papers, the program committee invited the following researchers to give plenary lectures at the workshop: C. Leiserson, N. Magnenat-Thalmann, M. Snir, U. Vazarani, and 1. Vitter. On behalf of the program committee, we would like to express our appreciation to the six plenary lecturers who accepted our invitation to speak, to all the authors who submitted papers to W ADS'99, and to the Pacific Institute for Mathematical Sciences for their sponsorship. Finally, we would like to express our gratitude to all the people who reviewed papers at the request of the program committee. August 1999 F. Dehne A. Gupta J.-R. Sack R. Tamassia VI Conference Chair: A. Gupta Program Committee Chairs: F. Dehne, A. Gupta, J.-R. Sack, R. Tamassia Program Committee: A. Andersson, A. Apostolico, G. Ausiello, G. Bilardi, K. Clarkson, R. Cleve, M. Cosnard, L. Devroye, P. Dymond, M. Farach-Colton, P. Fraigniaud, M. Goodrich, A.

Author : James M. Abello
Publisher : American Mathematical Soc.
ISBN 13 : 9780821870938
Total Pages : 330 pages
Book Rating : 4.39/5 ( download)

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Download or read book External Memory Algorithms written by James M. Abello and published by American Mathematical Soc.. This book was released on 1999-01-01 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents new research results and current techniques for the design and analysis of external memory algorithms. Topics presented include problems in computational geometry, graph theory, data compression, disk scheduling, linear algebra, statistics, software libraries, text and string processing, visualization, wavelets, and industrial applications.

Author : Clifford A. Shaffer
Publisher :
ISBN 13 :
Total Pages : 536 pages
Book Rating : 4.65/5 ( download)

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Download or read book A Practical Introduction to Data Structures and Algorithm Analysis written by Clifford A. Shaffer and published by . This book was released on 2001 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical text contains fairly "traditional" coverage of data structures with a clear and complete use of algorithm analysis, and some emphasis on file processing techniques as relevant to modern programmers. It fully integrates OO programming with these topics, as part of the detailed presentation of OO programming itself.Chapter topics include lists, stacks, and queues; binary and general trees; graphs; file processing and external sorting; searching; indexing; and limits to computation.For programmers who need a good reference on data structures.

Author : Jeffrey S. Vitter
Publisher : Springer
ISBN 13 : 3540483187
Total Pages : 369 pages
Book Rating : 4.82/5 ( download)

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Download or read book Algorithm Engineering written by Jeffrey S. Vitter and published by Springer. This book was released on 2003-05-15 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Algorithm Engineering, WAE'99, held in London, UK in July 1999. The 24 revised full papers presented were carefully reviewed and selected from a total of 46 submissions. The papers present original research results in all aspects of algorithm engineering including implementation, experimental testing, fine-tuning of discrete algorithms, development of repositories of software, methodological issues such as standards for empirical research on algorithms and data structures, and issues in the process of converting user requirements into efficient algorithmic solutions and implementations.

Author : Marina Barsky
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608457958
Total Pages : 95 pages
Book Rating : 4.53/5 ( download)

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Download or read book Full-text (substring) Indexes in External Memory written by Marina Barsky and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, textual databases are among the most rapidly growing collections of data. Some of these collections contain a new type of data that differs from classical numerical or textual data. These are long sequences of symbols, not divided into well-separated small tokens (words). The most prominent among such collections are databases of biological sequences, which are experiencing today an unprecedented growth rate. Starting in 2008, the "1000 Genomes Project" has been launched with the ultimate goal of collecting sequences of additional 1,500 Human genomes, 500 each of European, African, and East Asian origin. This will produce an extensive catalog of Human genetic variations. The size of just the raw sequences in this catalog would be about 5 terabytes. Querying strings without well-separated tokens poses a different set of challenges, typically addressed by building full-text indexes, which provide effective structures to index all the substrings of the given strings. Since full-text indexes occupy more space than the raw data, it is often necessary to use disk space for their construction. However, until recently, the construction of full-text indexes in secondary storage was considered impractical due to excessive I/O costs. Despite this, algorithms developed in the last decade demonstrated that efficient external construction of full-text indexes is indeed possible. This book is about large-scale construction and usage of full-text indexes. We focus mainly on suffix trees, and show efficient algorithms that can convert suffix trees to other kinds of full-text indexes and vice versa. There are four parts in this book. They are a mix of string searching theory with the reality of external memory constraints. The first part introduces general concepts of full-text indexes and shows the relationships between them. The second part presents the first series of external-memory construction algorithms that can handle the construction of full-text indexes for moderately large strings in the order of few gigabytes. The third part presents algorithms that scale for very large strings. The final part examines queries that can be facilitated by disk-resident full-text indexes. Table of Contents: Structures for Indexing Substrings / External Construction of Suffix Trees / Scaling Up: When the Input Exceeds the Main Memory / Queries for Disk-based Indexes / Conclusions and Open Problems

Author : Steve Scargall
Publisher : Apress
ISBN 13 : 1484249321
Total Pages : 384 pages
Book Rating : 4.21/5 ( download)

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Download or read book Programming Persistent Memory written by Steve Scargall and published by Apress. This book was released on 2020-01-09 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK)Design and optimize data structures for persistent memoryStudy how real-world applications are modified to leverage persistent memoryUtilize the tools available for persistent memory programming, application performance profiling, and debugging Who This Book Is For C, C++, Java, and Python developers, but will also be useful to software, cloud, and hardware architects across a broad spectrum of sectors, including cloud service providers, independent software vendors, high performance compute, artificial intelligence, data analytics, big data, etc.