Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Space Efficient Data Structures Streams And Algorithms
Download Space Efficient Data Structures Streams And Algorithms full books in PDF, epub, and Kindle. Read online Space Efficient Data Structures Streams And Algorithms ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Space-Efficient Data Structures, Streams, and Algorithms by : Andrej Brodnik
Download or read book Space-Efficient Data Structures, Streams, and Algorithms written by Andrej Brodnik and published by Springer. This book was released on 2013-08-13 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Festschrift volume, published in honour of J. Ian Munro, contains contributions written by some of his colleagues, former students, and friends. In celebration of his 66th birthday the colloquium "Conference on Space Efficient Data Structures, Streams and Algorithms" was held in Waterloo, ON, Canada, during August 15-16, 2013. The articles presented herein cover some of the main topics of Ian's research interests. Together they give a good overall perspective of the last 40 years of research in algorithms and data structures.
Book Synopsis Space Efficient Data Structures and Algorithms in the Word-ram Model by : Hicham El-Zein
Download or read book Space Efficient Data Structures and Algorithms in the Word-ram Model written by Hicham El-Zein and published by . This book was released on 2018 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: In modern computation the volume of data-sets has increased dramatically. Since the majority of these data-sets are stored in internal memory, reducing their storage requirement is an important research topic. One way of reducing storage is using succinct and compact data structures which maintain the data in compressed form with extra data structures over it in a way that allows efficient access and query of the data. In this thesis we study space-efficient data structures for various combinatorial objects. We focus on succinct and compact data structures. Succinct data structures are data structures whose size is within the information theoretic lower bound plus a lower order term, whereas compact data structures are data structures whose size is a constant factor from the information theoretic lower bound.
Book Synopsis Approximation and Online Algorithms by : Evripidis Bampis
Download or read book Approximation and Online Algorithms written by Evripidis Bampis and published by Springer. This book was released on 2010-05-09 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post workshop proceedings of the 7th International Workshop on Approximation and Online Algorithms, WAOA 2009, held in Copenhagen, Denmark, in September 2009 as part of the ALGO 2009 conference event. The 22 revised full papers presented were carefully reviewed and selected from 62 submissions. The workshop covered areas such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, parameterized complexity, randomization techniques, real-world applications, and scheduling problems.
Book Synopsis Algorithms and Data Structures for Massive Datasets by : Dzejla Medjedovic
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
Book Synopsis Algorithms and Data Structures for External Memory by : Jeffrey Scott Vitter
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.
Book Synopsis Probabilistic Data Structures and Algorithms for Big Data Applications by : Andrii Gakhov
Download or read book Probabilistic Data Structures and Algorithms for Big Data Applications written by Andrii Gakhov and published by BoD – Books on Demand. This book was released on 2022-08-05 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications. The purpose of this book is to introduce technology practitioners, including software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms. Reading this book, you will get a theoretical and practical understanding of probabilistic data structures and learn about their common uses.
Book Synopsis WALCOM: Algorithms and Computation by : Gautam K. Das
Download or read book WALCOM: Algorithms and Computation written by Gautam K. Das and published by Springer. This book was released on 2019-02-20 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 13th International Conference and Workshop on Algorithms and Computation, WALCOM 2019, held in Guwahati, India, in February/ March 2019. The 30 full papers presented were carefully reviewed and selected from 100 submissions. The papers are organized in topical headings on the facility location problem; computational geometry; graph drawing; graph algorithms; approximation algorithms; miscellaneous; data structures; parallel and distributed algorithms; and packing and covering.
Book Synopsis String Processing and Information Retrieval by : Nieves R. Brisaboa
Download or read book String Processing and Information Retrieval written by Nieves R. Brisaboa and published by Springer Nature. This book was released on 2019-10-05 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the 26th International Symposium on String Processing and Information Retrieval, SPIRE 2019, held in Segovia, Spain, in October 2019. The 28 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 59 submissions. They cover topics such as: data compression; information retrieval; string algorithms; algorithms; computational biology; indexing and compression; and compressed data structures.
Book Synopsis Advanced Data Mining and Applications by : Gao Cong
Download or read book Advanced Data Mining and Applications written by Gao Cong and published by Springer. This book was released on 2017-10-30 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.
Book Synopsis Space Efficient Data Structures for Collections of Textual Data by : Giuseppe Ottaviano
Download or read book Space Efficient Data Structures for Collections of Textual Data written by Giuseppe Ottaviano and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Structures and Algorithms 1 by : K. Mehlhorn
Download or read book Data Structures and Algorithms 1 written by K. Mehlhorn and published by Springer Science & Business Media. This book was released on 2013-11-09 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.
Book Synopsis Computing and Combinatorics by : Lusheng Wang
Download or read book Computing and Combinatorics written by Lusheng Wang and published by Springer. This book was released on 2018-06-29 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Conference on Computing and Combinatorics, COCOON 2018, held in Qing Dao, China, in July 2018. The 62 papers presented in this volume were carefully reviewed and selected from 120 submissions. They deal with the areas of algorithms, theory of computation, computational complexity, and combinatorics related to computing.
Book Synopsis Java: Data Structures and Programming by : Liwu Li
Download or read book Java: Data Structures and Programming written by Liwu Li and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to the Java language integrates a discussion of object-oriented programming with the design and implementation of data structures. It covers the most important topics, including algorithm analysis; time and space complexities; Java built-in data structure classes; input and output, data, and access streams; and the persistency of data.
Book Synopsis Data Classification by : Charu C. Aggarwal
Download or read book Data Classification written by Charu C. Aggarwal and published by CRC Press. This book was released on 2014-07-25 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi
Download or read book Online Algorithms written by Rahul Vaze and published by Cambridge University Press. This book was released on 2023-09-30 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a rigorous introduction to online algorithms for graduate and senior undergraduate students. In-depth coverage of most of the important topics is presented with special emphasis on elegant analysis. A wide range of solved examples and practice exercises are included, allowing hands-on exposure to the basic concepts.
Book Synopsis Using Additional Information in Streaming Algorithms by : Raffael Buff
Download or read book Using Additional Information in Streaming Algorithms written by Raffael Buff and published by diplom.de. This book was released on 2016-10-04 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis New Techniques for Adaptive Program Optimization by : Daniele Cono D’Elia
Download or read book New Techniques for Adaptive Program Optimization written by Daniele Cono D’Elia and published by Sapienza Università Editrice. This book was released on 2020-06-30 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Living organisms are adapted to their environment. Modern compilers and runtime systems for computer software are no different: as part of a continuous optimization process, they can adapt the execution cycle of a program to the workload it operates on. This thesis brings novel ideas to the software optimization domain. It illustrates methodological and practical contributions that advance the state of the art for performance profiling techniques and adaptive runtime designs, backed by promising experimental results on industrial-strength benchmarks. Part of the results has been presented in flagship programming language venues.
