Disk-Based Algorithms for Big Data(Hardcover)

Disk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing.

Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structures and MongoDB for unstructured document data.

Designed for senior undergraduate and graduate students, as well as professionals, this book is useful for anyone interested in understanding the foundations and advances in big data storage and management, and big data analytics.

Dr. Christopher G. Healey is a tenured Professor in the Department of Computer Science and the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics, both at North Carolina State University in Raleigh, North Carolina. He has published over 50 articles in major journals and conferences in the areas of visualization, visual and data analytics, computer graphics, and artificial intelligence. He is a recipient of the National Science Foundation’s CAREER Early Faculty Development Award and the North Carolina State University Outstanding Instructor Award. He is a Senior Member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and an Associate Editor of ACM Transaction on Applied Perception, the leading worldwide journal on the application of human perception to issues in computer science.

《大數據的磁碟式演算法》是近年來在大數據、數據分析以及支援儲存和分析大型數據集的底層檔案系統和數據管理演算法方面的最新進展的產物。本書討論了硬碟及其對數據管理的影響，因為硬碟驅動器在大型數據集群中仍然很常見。它還探討了通過主要和次要索引存儲和檢索數據的方法。這包括對不同的內存排序和搜索演算法的回顧，為更複雜的磁碟式方法（如合併排序、B樹和可擴展哈希）奠定了基礎。

在介紹之後，本書轉向更近期的主題，包括固態硬碟和全息存儲等先進存儲技術；點對點（P2P）通信；大型檔案系統和像Hadoop/HDFS、Hive、Cassandra和Presto這樣的查詢語言；以及Neo4j用於圖結構和MongoDB用於非結構化文檔數據的NoSQL數據庫。

本書適用於高年級本科生、研究生以及專業人士，對於任何對大數據存儲和管理以及大數據分析的基礎和進展感興趣的人都有用。

關於作者：
Christopher G. Healey博士是北卡羅來納州立大學（North Carolina State University）計算機科學系的終身教授，也是該校高級分析學院（Institute for Advanced Analytics）的Goodnight傑出教授。他在可視化、視覺和數據分析、計算機圖形學和人工智能等領域的主要期刊和會議上發表了50多篇文章。他是美國國家科學基金會（National Science Foundation）的CAREER早期教職發展獎和北卡羅來納州立大學傑出講師獎的獲得者。他是計算機機械學會（ACM）和電機電子工程師學會（IEEE）的高級會員，也是ACM Transaction on Applied Perception的副編輯，該期刊是全球領先的將人類感知應用於計算機科學問題的期刊。