| 书目名称 | Models of Computation for Big Data |
| 编辑 | Rajendra Akerkar |
| 视频video | |
| 概述 | Focuses on the fundamental principles of algorithm design for big data processing.Covers advanced models of computation relevant for developing memory-efficient algorithms.Advanced-level students and |
| 丛书名称 | Advanced Information and Knowledge Processing |
| 图书封面 |  |
| 描述 | .The big data tsunami changes the perspective of industrial and academic research in how they address both foundational questions and practical applications. This calls for a paradigm shift in algorithms and the underlying mathematical techniques. There is a need to understand foundational strengths and address the state of the art challenges in big data that could lead to practical impact. The main goal of this book is to introduce algorithmic techniques for dealing with big data sets. Traditional algorithms work successfully when the input data fits well within memory. In many recent application situations, however, the size of the input data is too large to fit within memory...Models of Computation for Big Data,. covers mathematical models for developing such algorithms, which has its roots in the study of big data that occur often in various applications. Most techniques discussed come from research in the last decade. The book will be structured as a sequence of algorithmic ideas, theoretical underpinning, and practical use of that algorithmic idea. Intended for both graduate students and advanced undergraduate students, there are no formal prerequisites, but the reader should |
| 出版日期 | Book 2018 |
| 关键词 | Big Data Algorithms; Streaming Algorithms; Sublinear Time Algorithms; Algorithmic Techniquesfor Big Dat |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-91851-8 |
| isbn_softcover | 978-3-319-91850-1 |
| isbn_ebook | 978-3-319-91851-8Series ISSN 1610-3947 Series E-ISSN 2197-8441 |
| issn_series | 1610-3947 |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2018 |
发表于 2025-3-21 19:51:38
