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Algorithms and data structures for large datasets Compass Algorithms
AMD 15626
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Understand the basic algorithms of large-scale data systems with rich illustrations!
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Информация о продукте
- 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?
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Data Scientists
This book provides essential algorithms for analyzing large datasets, crucial for data-driven decision-making in science.
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Software Engineers
Useful for engineers working on applications that manage or process big data, enhancing efficiency and performance.
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Computer Science Students
Offers foundational knowledge on algorithms and data structures, vital for academic success in computer science.
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Beginners
Novices may struggle with complex concepts presented in the book without prior knowledge of algorithms and data structures.
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Casual Readers
Those seeking light reading or quick insights into programming will find this book too technical and dense.
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Non-technical Users
Individuals without a technical background may not benefit from the content, which requires specialized knowledge to understand.
ОПИСАНИЕ ТОВАРА
Вопросы и ответы клиентов
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вопрос:
Who is the target audience for this book?
отвечать: The target audience includes readers with knowledge of basic data structures, algorithms, programming, and probability theory. -
вопрос:
What topics does the book cover?
отвечать: The book covers probabilistic data structures, streaming algorithms, and external storage data structures. -
вопрос:
Is this book suitable for beginners?
отвечать: 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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AMD 15626
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Особенности и преимущества
- 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.






