Big Data In Complex Systems
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Big Data In Complex Systems
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer
Release Date : 2015-01-02
Big Data In Complex Systems written by Aboul Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-02 with Technology & Engineering categories.
This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.
Data Driven Modeling Scientific Computation
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Author : J. Nathan Kutz
language : en
Publisher: Oxford University Press
Release Date : 2013-08-08
Data Driven Modeling Scientific Computation written by J. Nathan Kutz and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-08 with Computers categories.
Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.
Big Data Conceptual Analysis And Applications
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Author : Michael Z. Zgurovsky
language : en
Publisher: Springer
Release Date : 2019-03-20
Big Data Conceptual Analysis And Applications written by Michael Z. Zgurovsky and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-20 with Technology & Engineering categories.
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.
Big Data In Complex And Social Networks
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Author : My T. Thai
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2016-01-15
Big Data In Complex And Social Networks written by My T. Thai and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-15 with Business & Economics categories.
The ultimate objective of this book is to provide the academic and industrial communities with an in-depth introduction to the recent advances in development, theory, and impacts of Big Data, tailored to applications in complex and social networks. Specifically, the book focuses on how to retrieve, store, manipulate and analyze data and how to develop new tools and techniques to study and visualize massive datasets in the domain of complex and social networks.
Big Data In Complex And Social Networks
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Author : My T. Thai
language : en
Publisher: CRC Press
Release Date : 2016-12-01
Big Data In Complex And Social Networks written by My T. Thai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-01 with Business & Economics categories.
This book presents recent developments on the theoretical, algorithmic, and application aspects of Big Data in Complex and Social Networks. The book consists of four parts, covering a wide range of topics. The first part of the book focuses on data storage and data processing. It explores how the efficient storage of data can fundamentally support intensive data access and queries, which enables sophisticated analysis. It also looks at how data processing and visualization help to communicate information clearly and efficiently. The second part of the book is devoted to the extraction of essential information and the prediction of web content. The book shows how Big Data analysis can be used to understand the interests, location, and search history of users and provide more accurate predictions of User Behavior. The latter two parts of the book cover the protection of privacy and security, and emergent applications of big data and social networks. It analyzes how to model rumor diffusion, identify misinformation from massive data, and design intervention strategies. Applications of big data and social networks in multilayer networks and multiparty systems are also covered in-depth.
Big Data In Organizations And The Role Of Human Resource Management
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Author : Tobias M. Scholz
language : en
Publisher: Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften
Release Date : 2017
Big Data In Organizations And The Role Of Human Resource Management written by Tobias M. Scholz and has been published by Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Business & Economics categories.
Big data are changing the way we work. This book conveys a theoretical understanding of big data and the related interactions on a socio-technological level as well as on the organizational level. Big data challenge the human resource department to take a new role. An organization's new competitive advantage is its employees augmented by big data.
Big Data Modeling And Monitoring In Complex Systems
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Author : Xiaochen Xian
language : en
Publisher:
Release Date : 2019
Big Data Modeling And Monitoring In Complex Systems written by Xiaochen Xian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
In modern complex systems, massive quantities of temporally and spatially dense data are frequently generated due to the rapid advancements of sensor technology and communication networks. Such a data-rich environment poses new and significant challenges of analysis in the following aspects: (i) the access and efficient handling of the rich and heterogeneous data streams that may be contaminated by noises, (ii) the recognition of system knowledge that describes numerous components, complicated interactions, and ever-changing dynamics, and (iii) the effective implementation of the acquired knowledge to enhanced modeling, monitoring, predicting and control of the systems. This thesis concentrates on big data modeling and monitoring to develop systematic data-driven analytics methodologies for process modeling, quality control, and performance improvement in complex systems. By incorporating engineering domain knowledge with advanced techniques in statistics and machine learning, the proposed methodologies facilitate (i) the identification of appropriate and robust models that describe the system structures and dynamics, (ii) the effective surveillance of system status, (iii) the more accurate forecasting of future trends and dynamics, and (iv) the informative decisions that improve the system productivity and performance. The first chapter introduces the background and challenges in big data modeling and monitoring is complex systems, and establishes the major research objective of the thesis. Chapter 2 addresses the causation-based monitoring and diagnosis for multivariate categorical processes that integrates ordinal information. In this chapter, we consider the applications where there exists both causal relationship and different types of categorical variables. Bayesian networks are leveraged to characterize multivariate categorical processes with a causal structure, where the categorical variables can be either nominal, ordinal, or a combination of both. In Chapter 3, a monitoring and x sampling strategy is proposed to online monitor non-normal big data streams in the context of limited resources, where only a subset of observations is available at each acquisition time. In particular, the proposed method integrates a rank-based CUSUM scheme and an innovative idea that corrects the anti-rank statistics with partial observations, which can effectively detect a wide range of possible mean shifts when data streams are exchangeable and follow arbitrary distributions. Chapter 4 further proposes a rank-based monitoring and sampling algorithm based on data augmentation to quickly detect the mean shifts in a process when only a limited portion of observations is available online. Specifically, at each observation time, the proposed method will automatically augment the optimal information for unobservable variables based on the online observations, and then intelligently allocate the monitoring resources to the most suspicious data streams. Chapter 5 proposes a modeling and prediction method for traffic demand counts for origin-destination (OD) pairs. A multivariate Poisson log-normal model is formulated with specific parametrization tailored to the traffic demand problem, which captures the spatiotemporal correlations of the traffic demand across different routes and epochs, and automatically clusters the routes based on the demand correlations. The model is further estimated using an Expectation- Maximization (EM) algorithm and applied for predicting future demand counts at the subsequent epochs. The estimation and prediction procedures incorporate Markov chain Monte Carlo (MCMC) sampling to overcome the computational challenges. Chapter 6 then summarizes the contribution of the thesis. In summary, this thesis contributes to big data modeling and monitoring to develop systematic data-driven analytics methodologies for process modeling, quality control, and performance improvement in complex systems. The generic research leads to immediate applications in manufacturing, healthcare, transportation, climate, energy and service systems, etc.
Big Data Analytics
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Author : Saumyadipta Pyne
language : en
Publisher: Springer
Release Date : 2016-10-12
Big Data Analytics written by Saumyadipta Pyne and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-12 with Computers categories.
This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
Metasynthetic Computing And Engineering Of Complex Systems
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Author : Longbing Cao
language : en
Publisher: Springer
Release Date : 2015-05-29
Metasynthetic Computing And Engineering Of Complex Systems written by Longbing Cao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-29 with Computers categories.
Provides a comprehensive overview and introduction to the concepts, methodologies, analysis, design and applications of metasynthetic computing and engineering. The author: • Presents an overview of complex systems, especially open complex giant systems such as the Internet, complex behavioural and social problems, and actionable knowledge discovery and delivery in the big data era. • Discusses ubiquitous intelligence in complex systems, including human intelligence, domain intelligence, social intelligence, network intelligence, data intelligence and machine intelligence, and their synergy through metasynthetic engineering. • Explains the concept and methodology of human-centred, human-machine-cooperated qualitative-to-quantitative metasynthesis for understanding and managing open complex giant systems, and its computing approach: metasynthetic computing. • Introduces techniques and tools for analysing and designing problem-solving systems for open complex problems and systems. Metasynthetic Computing and Engineering uses the systematology methodology in addressing system complexities in open complex giant systems, for which it may not only be effective to apply reductionism or holism. The book aims to encourage and inspire discussions, design, implementation and reflection of effective methodologies and tools for computing and engineering open complex systems and problems. Researchers, research students and practitioners in complex systems, artificial intelligence, data science, computer science, and even system science, cognitive science, behaviour science, and social science, will find this book invaluable.
Data Science For Complex Systems
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Author : Anindya S. Chakrabarti
language : en
Publisher: Cambridge University Press
Release Date : 2023-04-30
Data Science For Complex Systems written by Anindya S. Chakrabarti and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-30 with Computers categories.
This book provides a guide to the analysis of complex systems through the lens of data science.