Advanced Filter Structure Handbook: From Design to Optimization

Advanced Filter Structure Handbook: From Design to Optimization is an essential resource for anyone involved in managing and processing large datasets. As data grows exponentially, efficient filtering techniques become increasingly critical to performance and resource management. This book provides a comprehensive guide to understanding and implementing advanced data filters, from traditional Bloom filters to cutting-edge innovations like Ribbon Filter, Bamboo Filter, and SNARF.

Unlike other texts that offer only a superficial overview, Advanced Data Filter Architectures delves deep into the structures, operations, and performance of both well-established and novel filters. Divided into four categories—read-only filters, space-elastic filters, streaming filters, and range filters—this book systematically explores the challenges and solutions associated with dynamic space partitioning, streaming data, and range queries. It offers detailed insights into the design principles and implementation methods necessary to develop high-performance filters tailored to specific applications.

Ideal for researchers, practitioners, and students in computer science, database management, and big data, this book is more than just a reference—it is a comprehensive toolkit for mastering advanced filtering technologies. By introducing innovative designs that push the boundaries of filter performance, this book not only equips readers with the knowledge to tackle current challenges but also inspires new avenues of research and development. Advanced Data Filter Architectures is a must-have for those looking to stay at the forefront of data filtering technology.

Haipeng Dai received the B.S. degree in the Department of Electronic Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2010, and the Ph.D. degree in the Department of Computer Science and Technology in Nanjing University, Nanjing, China, in 2014. His research interests are mainly in the areas of data mining, Internet of Things, and mobile computing. He is an associate professor in the School of Computer Science , Nanjing University.

Meng Li received his B.S. degree in Computer Science from Nanjing University, Jiangsu, China, in 2016 and the Ph.D. degree in the Department of Computer Science and Technology in Nanjing University, Nanjing, China, in 2023. He is currently an assistant researcher in the School of Computer Science, Nanjing University. His research interests are in the area of sketch-enabled high performance systems and network as well as IOT.

Guihai Chen received a B.S. degree in computer software from Nanjing University in 1984, an M.E. degree in computer applications from Southeast University in 1987, and a Ph.D. degree in computer science from the University of Hong Kong in 1997. He is a professor and deputy chair in the School of Computer Science, Nanjing University, China. He had been invited as a visiting professor by many foreign universities, including Kyushu Institute of Technology, Japan, in 1998, University of Queensland, Australia in 2000, and Wayne State University, USA from September 2001 to August 2003. He has a wide range of research interests focusing on sensor networks, peer-to-peer computing, high-performance computer architecture, and combinatorics.