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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 Universitat der Bundeswehr Munchen. 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 Universitat Munchen. 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.
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Human Systems Engineering and Design II - Proceedings of the 2nd International Conference on Human Systems Engineering and Design (IHSED2019): Future Trends and Applications, September 16-18, 2019, Universitat der Bundeswehr Munchen, Munich, Germany (Paperback, 1st ed. 2020)
Tareq Ahram, Waldemar Karwowski, Stefan Pickl, Redha Taiar
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R8,052
Discovery Miles 80 520
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Ships in 10 - 15 working days
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This book focuses on novel design and systems engineering
approaches, including theories and best practices, for promoting a
better integration of people and engineering systems. It covers a
range of hot topics related to: development of human-centered
systems; interface design and human-computer interaction; usability
and user experience; emergent properties of human behavior;
innovative materials in manufacturing, biomechanics, and sports
medicine, safety engineering and systems complexity business
analytics, design and technology and many more. The book, which
gathers selected papers presented at the 2nd International
Conference on Human Systems Engineering and Design: Future Trends
and Applications (IHSED 2019), held on September 16-18, 2019, at
Universitat der Bundeswehr Munchen, Munich, Germany, provides
researchers, practitioners and program managers with a snapshot of
the state-of-the-art and current challenges in the field of human
systems engineering and design.
This book presents the latest findings on stochastic dynamic
programming models and on solving optimal control problems in
networks. It includes the authors' new findings on determining the
optimal solution of discrete optimal control problems in networks
and on solving game variants of Markov decision problems in the
context of computational networks. First, the book studies the
finite state space of Markov processes and reviews the existing
methods and algorithms for determining the main characteristics in
Markov chains, before proposing new approaches based on dynamic
programming and combinatorial methods. Chapter two is dedicated to
infinite horizon stochastic discrete optimal control models and
Markov decision problems with average and expected total discounted
optimization criteria, while Chapter three develops a special
game-theoretical approach to Markov decision processes and
stochastic discrete optimal control problems. In closing, the
book's final chapter is devoted to finite horizon stochastic
control problems and Markov decision processes. The algorithms
developed represent a valuable contribution to the important field
of computational network theory.
This book presents the latest findings on stochastic dynamic
programming models and on solving optimal control problems in
networks. It includes the authors' new findings on determining the
optimal solution of discrete optimal control problems in networks
and on solving game variants of Markov decision problems in the
context of computational networks. First, the book studies the
finite state space of Markov processes and reviews the existing
methods and algorithms for determining the main characteristics in
Markov chains, before proposing new approaches based on dynamic
programming and combinatorial methods. Chapter two is dedicated to
infinite horizon stochastic discrete optimal control models and
Markov decision problems with average and expected total discounted
optimization criteria, while Chapter three develops a special
game-theoretical approach to Markov decision processes and
stochastic discrete optimal control problems. In closing, the
book's final chapter is devoted to finite horizon stochastic
control problems and Markov decision processes. The algorithms
developed represent a valuable contribution to the important field
of computational network theory.
Emissions trading challenges the management of companies in an
entirely new manner: Not only does it, like other market-based
environmental policy instruments, allow for a bigger flexibility in
management decisions concerning emission issues. More importantly,
it shifts the mode of governance of environmental policy from
hierarchy to market. But how is this change reflected in management
processes, decisions and organizational structures? The
contributions in this book discuss the theoretical implications of
different institutional designs of emissions trading schemes,
review schemes that have been implemented in the US and Europe, and
evaluate the range of investment decisions and corporate strategies
which have resulted from the new policy framework.
Emissions trading challenges the management of companies in an
entirely new manner: Not only does it, like other market-based
environmental policy instruments, allow for a bigger flexibility in
management decisions concerning emission issues. More importantly,
it shifts the mode of governance of environmental policy from
hierarchy to market. But how is this change reflected in management
processes, decisions and organizational structures? The
contributions in this book discuss the theoretical implications of
different institutional designs of emissions trading schemes,
review schemes that have been implemented in the US and Europe, and
evaluate the range of investment decisions and corporate strategies
which have resulted from the new policy framework.
This book contains selected papers from the symposium "Operations
Research 2010" which was held from September 1-3, 2010 at the
"Universit t der Bundeswehr M nchen," Germany. The international
conference, which also serves as the annual meeting of the German
Operations Research Society (GOR), attracted more than 600
participants from more than thirty countries. The general theme
"Mastering Complexity" focusses on a natural component of the
globalization process. Financial markets, traffic systems, network
topologies and, last but not least, energy resource management, all
contain complex behaviour and economic interdependencies which
necessitate a scientific solution. Operations Research is one of
the key instruments to model, simulate and analyze such systems. In
the process of developing optimal solutions, suitable heuristics
and efficient procedures are some of the challenges which are
discussed in this volume.
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 Universitat der Bundeswehr Munchen. 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 Universitat Munchen. 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.
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Modelling and Simulation for Autonomous Systems - 8th International Conference, MESAS 2021, Virtual Event, October 13-14, 2021, Revised Selected Papers (Paperback, 1st ed. 2022)
Jan Mazal, Adriano Fagiolini, Petr Vasik, Michele Turi, Agostino Bruzzone, …
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R3,031
Discovery Miles 30 310
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post-conference
proceedings of the 8th International Conference on Modelling and
Simulation for Autonomous Systems, MESAS 2021, held as a virtual
event due COVID-19, in October 2021.The 30 full papers together
with 2 short papers included in the volume were carefully reviewed
and selected from 50 submissions. They are organized in the
following topical sections: M&S of intelligent systems, R&D
and application; and AxS/AI in context of future warfare and
security environment and future challenges of Advance M&S
Technology.
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Modelling and Simulation for Autonomous Systems - 9th International Conference, MESAS 2022, Prague, Czech Republic, October 20–21, 2022, Revised Selected Papers (1st ed. 2023)
Jan Mazal, Adriano Fagiolini, Petr Vasik, Agostino G Bruzzone, Stefan Pickl, …
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R1,908
Discovery Miles 19 080
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Ships in 12 - 17 working days
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This book constitutes the thoroughly refereed post-conference
proceedings of the 9th International Conference on Modelling and
Simulation for Autonomous Systems, MESAS 2022, held MESAS 2022,
Prague, Czech Republic, October 2022.The 21 full papers included in
the volume were carefully reviewed and selected from 24
submissions. They are organized in the following topical sections:
Modelling, Simulation Technology, methodologies and Robotics.
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