Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
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Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Algorithms and data structures for large datasets Compass Algorithms
Understand the basic algorithms of large-scale data systems with rich illustrations!
Algorithms and data structures for large datasets Compass Algorithms
Προϊόν #: 87939395

Algorithms and data structures for large datasets Compass Algorithms

Προϊόν #: 87939395

€ 36

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What Stands Out

Comprehensive Coverage
This book offers an in-depth exploration of algorithms and data structures tailored for large datasets, making it essential for data scientists and engineers seeking effective solutions in complex scenarios.
Practical Applications
Packed with real-world examples, this guide enables readers to apply theoretical knowledge directly to industry challenges, bridging the gap between academia and practical implementation of large-scale data processing.
User-Friendly Format
The Softcover edition ensures portability and ease of use, providing an accessible format for studying on-the-go, catering to students and professionals alike who need flexibility in their learning resources.

Λεπτομέρειες προιόντος

Shop Algorithms and data structures for large datasets Compass Algorithms online at a best price in Greece. 4839984166
  • Understand the basic algorithms of large-scale data systems with rich illustrations! A guidebook for understanding the algorithmic elements underlying large-scale data systems and building scaleable applications. We will explain it in an easy-to-understand manner with a wealth of illustrations! How to use stochastic data structures to save space for data storage, processing streaming data, manipulating data on disk, understanding performance trade-offs in database systems, and more in building large-scale applications. It covers various algorithmic aspects. ［target audience］ It is aimed at readers who understand basic data structures and algorithms. After each chapter presents a traditional solution, it explains why it doesn't work in large-scale data situations. Those who have knowledge of programming and the basics of probability theory ・Those who have knowledge of understanding Python and pseudocode. ［configuration］ This book has 11 chapters and consists of three parts. The first part is about probabilistic and concise data structures, the second part is about streaming data structures and algorithms, and the third part is about external storage data structures and algorithms. Chapter 1 Introduction Part 1 Hash-based sketch Chapter 2 Overview of hash tables and modern hashing Chapter 3 Determining the existence of approximate data: Bloom filter and quotient filter Chapter 4 Frequency Estimation and Counting Min Sketch Chapter 5 Cardinality estimation and hyperloglog Part 2 Real-time analysis Chapter 6 Streaming Data Integration and Applications Chapter 7 Sampling from data streams Chapter 8 Approximate quantiles on a data stream Part 3 Data structures for databases and external storage algorithms Chapter 9 Introduction to external memory models Chapter 10 Data structure for database: B-tree, B-e-tree, LSM-tree Chapter 11 Sorting by external memory
Publisher マイナビ出版
Publication date July 26, 2024
Language Japanese
Print length 328 pages
ISBN-10 4839984166
ISBN-13 978-4839984168
Item Weight 630 g
Dimensions 0.91 x 7.17 x 9.21 inches (2.3 x 18.2 x 23.4 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    This book provides essential algorithms for analyzing large datasets, crucial for data-driven decision-making in science.

  • Software Engineers

    Useful for engineers working on applications that manage or process big data, enhancing efficiency and performance.

  • Computer Science Students

    Offers foundational knowledge on algorithms and data structures, vital for academic success in computer science.

Not Suitable For
  • Beginners

    Novices may struggle with complex concepts presented in the book without prior knowledge of algorithms and data structures.

  • Casual Readers

    Those seeking light reading or quick insights into programming will find this book too technical and dense.

  • Non-technical Users

    Individuals without a technical background may not benefit from the content, which requires specialized knowledge to understand.

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Ερωτήσεις & Απαντήσεις Πελατών

  • ερώτηση: Who is the target audience for this book?

    απάντηση: The target audience includes readers with knowledge of basic data structures, algorithms, programming, and probability theory.
  • ερώτηση: What topics does the book cover?

    απάντηση: The book covers probabilistic data structures, streaming algorithms, and external storage data structures.
  • ερώτηση: Is this book suitable for beginners?

    απάντηση: This book is not designed for absolute beginners; a basic understanding of algorithms and Python is recommended.

Dzejla Medjedovic , Emin Tahirovic , Ines Dedovic Data Processing Editorial Review

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€ 36

Παραγγείλτε τώρα και πάρτε το περίπου: Σάββατο, Ιούλιος 04

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Μόνο 8 προϊόντα έχουν απομείνει σε απόθεμα.
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Χαρακτηριστικά & Πλεονεκτήματα

  • Comprehensive guidebook for large-scale data applications.
  • Easy-to-understand explanations with rich illustrations.
  • Covers probabilistic, streaming, and external storage data structures.
  • Ideal for readers with basic programming and data structure knowledge.
  • Explains traditional solutions and their limitations in large-scale contexts.
  • Build scalable applications effectively with proven algorithms.