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High Performance Networking, Computing, and Communication Systems
This book constitutes the refereed post-proceedings of the Second International Conference on High Performance Networking, Computing, and Communication systems, ICHCC 2011, held in Singapore in May 2011. The conference was held together with the Second International Conference on Theoretical and Mathematical Foundations of Computer Science, ICTMF 2011, which proceedings are published in CCIS 164. The 84 revised selected papers presented were carefully reviewed and selected for inclusion in the book. The topics covered range from computational science, engineering and technology to digital signal processing, and computational biology to game theory, and other related topices.
Cloud Services, Networking, and Management
Cloud Services, Networking and Management provides a comprehensive overview of the cloud infrastructure and services, as well as their underlying management mechanisms, including data center virtualization and networking, cloud security and reliability, big data analytics, scientific and commercial applications. Special features of the book include: State-of-the-art content Self-contained chapters for readers with specific interests Includes commercial applications on Cloud (video services and games)
Social Informatics
This book constitutes the proceedings of the Third International Conference on Social Informatics, SocInfo 2011, held in Singapore in October 2011. The 15 full papers, 8 short papers and 13 posters included in this volume were carefully reviewed and selected from 68 full paper and 13 poster submissions. The papers are organized in topical sections named: network analysis; eGovernance and knowledge management; applications of network analysis; community dynamics; case studies; trust, privacy and security; peer-production.
Machine Learning in Cyber Trust
Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work. Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
Social Informatics
The two-volume set LNCS 11185 + 11186 constitutes the proceedings of the 10th International Conference on Social Informatics, SocInfo 2018, held in Saint-Petersburg, Russia, in September 2018. The 30 full and 32 short papers presented in these proceedings were carefully reviewed and selected from 110 submissions. They deal with the applications of methods of the social sciences in the study of socio-technical systems, and computer science methods to analyze complex social processes, as well as those that make use of social concepts in the design of information systems.
Knowledge Graphs
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced ...
Social Informatics
This volume constitutes the proceedings of the 11th International Conference on Social Informatics, SocInfo 2019, held in Doha, Qatar, in November 2019. The 17 full and 5 short papers presented in these proceedings were carefully reviewed and selected from 86 submissions. The papers presented in this volume cover a broad range of topics, ranging from the study of socio-technical systems, to computer science methods to analyze complex social processes, as well as social concepts in the design of information systems.
Social Informatics
This book constitutes the proceedings of the 6th International Conference on Social Informatics, SocInfo 2014, held in Barcelona, Spain, in November 2014. The 28 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections such as network, communities, and crowds; interpersonal links and gender biases; news, credibility, and opinion formation; science and technologies; organizations, society and social good.
Ancestry
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Massive Graph Analytics
"Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics." — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national laboratories, and industry who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive-scale graph analytics.
