Algorithms for Big Data: DFG Priority Program 1736

by Hannah Bast (Editor), Claudius Korzen (Editor), Ulrich Meyer (Editor), Manuel Penschuck (Editor)

Overview

This open access book surveys the progress in addressing selected challenges related to the growth of big data in combination with increasingly complicated hardware.

It emerged from a research program established by the German Research Foundation (DFG) as priority program SPP 1736 on Algorithmics for Big Data where researchers from theoretical computer science worked together with application experts in order to tackle problems in domains such as networking, genomics research, and information retrieval. Such domains are unthinkable without substantial hardware and software support, and these systems acquire, process, exchange, and store data at an exponential rate.
The chapters of this volume summarize the results of projects realized within the program and survey-related work.

This is an open access book.

Product Details

ISBN-13:	9783031215339
Publisher:	Springer Nature Switzerland
Publication date:	01/18/2023
Series:	Lecture Notes in Computer Science , #13201
Edition description:	1st ed. 2022
Pages:	285
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

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.

Editorial Reviews

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)

From the Publisher