Algorithms for Big Data

“This book covers a wide range of topics in big data research. If I were running a master’s program in big data, I would use this book as a source for dissertations. It’s hard to envisage anyone (except perhaps a starting PhD student wanting to get a feel for the range of big data research) reading the entire book, but the individual papers will have their own readerships.” (J. H. Davenport, Computing Reviews, February 7, 2024)

• Algorithms for Large and Complex Networks Algorithms for Large-scale Network Analysis and the NetworKit Toolkit.- Generating Synthetic Graph Data from Random Network Models.- Sampling Efficiency for the Link Assessment Problem.- A Custom Hardware Architecture for the Link Assessment Problem.- Graph-based Methods for Rational Drug Design.- Recent Advances in Practical Data Reduction.- Skeleton-based Clustering by Quasi-Threshold Editing.- The Space Complexity of Undirected Graph Exploration.- Algorithms for Big Data and their Applications Scalable Cryptography.- Distributed Data Streams.- Energy-Efficient Scheduling.- The GENO Software Stack.- Laue Algorithms for Big Data Problems in de Novo Genome Assembly.- Scalable Text Index Construction. Big Data, Scalability, Algorithms, Applications, Graphs, Networks, Parallelism, Distributed, Memory Hierarchy, Algorithm Engineering, Network Analysis, Random Graphs, Graph Clustering, Data Streams, Cryptography, Energy Efficiency, Text Indices.