Data Structure Techniques
Author: Thomas A. Standish
Publisher: Addison Wesley Publishing Company
Published: 1980
Total Pages: 472
ISBN-13:
DOWNLOAD EBOOK →Author: Thomas A. Standish
Publisher: Addison Wesley Publishing Company
Published: 1980
Total Pages: 472
ISBN-13:
DOWNLOAD EBOOK →Author: Dzejla Medjedovic
Publisher: Simon and Schuster
Published: 2022-08-16
Total Pages: 302
ISBN-13: 1638356564
DOWNLOAD EBOOK →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: Jay Wengrow
Publisher: Pragmatic Bookshelf
Published: 2020-08-10
Total Pages: 714
ISBN-13: 1680508059
DOWNLOAD EBOOK →Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable.
Author: Harry R. Lewis
Publisher: Addison Wesley
Published: 1991
Total Pages: 536
ISBN-13:
DOWNLOAD EBOOK →Using only practically useful techniques, this book teaches methods for organizing, reorganizing, exploring, and retrieving data in digital computers, and the mathematical analysis of those techniques. The authors present analyses that are relatively brief and non-technical but illuminate the important performance characteristics of the algorithms. Data Structures and Their Algorithms covers algorithms, not the expression of algorithms in the syntax of particular programming languages. The authors have adopted a pseudocode notation that is readily understandable to programmers but has a simple syntax.
Author: Edward M. Reingold
Publisher:
Published: 1983
Total Pages: 474
ISBN-13:
DOWNLOAD EBOOK →Data structures are central to computer science, and in particular to programming. In the analytic areas, appropriate data structures have been the key to advances in the design of algorithms. Once appropriate data structures are carefully defined, all that remains is routine coding. A comprehensive understanding of data structure techniques is essential in the design of algorithms and programs. This text presents a carefully chosen fraction of available material, but supplement it with a wide variety of exercises. No single book can discuss all known data structures or algorithms. This text presents the art of designing data structures, preparing the student to devise special-purpose structures for specific problems as they present themselves.
Author: Hermann A. Maurer
Publisher: Prentice Hall
Published: 1977
Total Pages: 246
ISBN-13:
DOWNLOAD EBOOK →Author: Dr. Anand K. Tripathi
Publisher: Laxmi Publications, Ltd.
Published: 2010
Total Pages: 118
ISBN-13: 9789380386393
DOWNLOAD EBOOK →Author: Mark H. Overmars
Publisher: Springer Science & Business Media
Published: 1983
Total Pages: 194
ISBN-13: 9783540123309
DOWNLOAD EBOOK →In numerous computer applications there is a need of storing large sets of objects in such a way that some questions about those objects can be answered efficiently. Data structures that store such sets of objects can be either static (built for a fixed set of objects) or dynamic (insertions of new objects and deletions of existing objects can be performed). Especially for more complex searching problems as they arise in such fields as computational geometry, database design and computer graphics, only static data structures are available. This book aims at remedying this lack of flexibility by providing a number of general techniques for turning static data structures for searching problems into dynamic structures. Although the approach is basically theoretical, the techniques offered are often practically applicable. The book is written in such a way that it is readable for those who have some elementary knowledge of data structures and algorithms. Although this monograph was first published in 1983, it is still unique as a general treatment of methods for constructing dynamic data structures.
Author: Pat Morin
Publisher: Athabasca University Press
Published: 2013
Total Pages: 336
ISBN-13: 1927356385
DOWNLOAD EBOOK →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: Clifford A. Shaffer
Publisher:
Published: 2001
Total Pages: 536
ISBN-13:
DOWNLOAD EBOOK →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.