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Bester Preis: € 97,64 (vom 14.04.2017)1
Big Data of Complex Networks
EN NWISBN: 9781498723619 bzw. 1498723616, in Englisch, Taylor & Francis Inc, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, in-stock.
This book presents and explains the methods utilized to apply techniques from big data to massive structural data sets, especially those relating to the generation and analysis of very large networks or large sets of graphs. It is evident that the analysis of massive data representing complex networks may often involve statistical analyses such as sampling and bootstrapping. Novel techniques may be applied to the existing concepts in an interdisciplinary way. Special aspects such as computer memory are also investigated.
This book presents and explains the methods utilized to apply techniques from big data to massive structural data sets, especially those relating to the generation and analysis of very large networks or large sets of graphs. It is evident that the analysis of massive data representing complex networks may often involve statistical analyses such as sampling and bootstrapping. Novel techniques may be applied to the existing concepts in an interdisciplinary way. Special aspects such as computer memory are also investigated.
2
Big Data of Complex Networks
EN NW EBISBN: 9781498723619 bzw. 1498723616, in Englisch, CRC Press, neu, E-Book.
Lieferung aus: Vereinigte Staaten von Amerika, Ebook for download.
Computers, Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT The Health and Life Sciences University, Austria, and the Universitt der Bundeswehr Mnchen. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universitt Mnchen. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology. eBook.
Computers, Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT The Health and Life Sciences University, Austria, and the Universitt der Bundeswehr Mnchen. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universitt Mnchen. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology. eBook.
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