Network Science Models For Data Analytics Automation

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Network Science Models for Data Analytics Automation

Xin W. Chen
Network Science Models for Data Analytics Automation Book PDF

Author : Xin W. Chen
Publisher : Springer Nature
Page : 126 pages
File Size : 45,9 Mb
Release : 2022-02-21
Category : Technology & Engineering
ISBN : 9783030964702

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Network Science Models for Data Analytics Automation by Xin W. Chen Pdf

This book explains network science and its applications in data analytics for critical infrastructures, engineered systems, and knowledge acquisition. Each chapter describes step-by-step processes of how network science enables and automates data analytics through examples. The book not only dissects modeling techniques and analytical results but also explores the intrinsic development of these models and analyses. This unique approach bridges the gap between theory and practice and channels’ managerial and problem-solving skills. Engineers, researchers, and managers would benefit from the extensive theoretical background and practical examples discussed in this book. Advanced undergraduate students and graduate students in mathematics, statistics, engineering, business, public health, and social science may use this book as a one-semester textbook or a reference book. Readers who are more interested in applications may skip Chapter 1 and peruse through the rest of the book with ease.

Data Analytics for IT Networks

John Garrett
Data Analytics for IT Networks Book PDF

Author : John Garrett
Publisher : Cisco Press
Page : 743 pages
File Size : 44,6 Mb
Release : 2018-10-24
Category : Computers
ISBN : 9780135183441

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Data Analytics for IT Networks by John Garrett Pdf

Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data

Systems Collaboration and Integration

Chin-Yin Huang,Sang Won Yoon
Systems Collaboration and Integration Book PDF

Author : Chin-Yin Huang,Sang Won Yoon
Publisher : Springer Nature
Page : 500 pages
File Size : 52,6 Mb
Release : 2023-10-17
Category : Technology & Engineering
ISBN : 9783031443732

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Systems Collaboration and Integration by Chin-Yin Huang,Sang Won Yoon Pdf

This book is a groundbreaking exploration of the historical and contemporary challenges in systems collaboration and integration. This exceptional book delves into engineering design, planning, control, and management, offering invaluable insights into the evolving nature of systems and networks. In an era defined by the ongoing cyber and digital transformation, coupled with artificial intelligence and machine learning, this book offers insights into the future of systems collaboration and integration. Over the past three decades, the PRISM Center and its affiliated PRISM Global Research Network (PGRN) have spearheaded pioneering theories, technologies, and applications in the realm of systems collaboration and integration. Their research, driven by the motto “Knowledge through information; Wisdom through collaboration,” has yielded remarkable advancements. Those achievements and papers presented and updated by the PGRN scholars in the 26th ICPR are included in this book.

Springer Handbook of Automation

Shimon Y. Nof
Springer Handbook of Automation Book PDF

Author : Shimon Y. Nof
Publisher : Springer Nature
Page : 1533 pages
File Size : 54,9 Mb
Release : 2023-06-16
Category : Technology & Engineering
ISBN : 9783030967291

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Springer Handbook of Automation by Shimon Y. Nof Pdf

This handbook incorporates new developments in automation. It also presents a widespread and well-structured conglomeration of new emerging application areas, such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. The handbook is not only an ideal resource for automation experts but also for people new to this expanding field.

Web and Network Data Science

Thomas W. Miller
Web and Network Data Science Book PDF

Author : Thomas W. Miller
Publisher : FT Press
Page : 370 pages
File Size : 52,8 Mb
Release : 2014-12-19
Category : Business & Economics
ISBN : 9780133887648

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Web and Network Data Science by Thomas W. Miller Pdf

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Soft Computing for Data Analytics, Classification Model, and Control

Deepak Gupta,Aditya Khamparia,Ashish Khanna,Oscar Castillo
Soft Computing for Data Analytics Classification Model and Control Book PDF

Author : Deepak Gupta,Aditya Khamparia,Ashish Khanna,Oscar Castillo
Publisher : Springer Nature
Page : 165 pages
File Size : 53,5 Mb
Release : 2022-01-30
Category : Technology & Engineering
ISBN : 9783030920265

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Soft Computing for Data Analytics, Classification Model, and Control by Deepak Gupta,Aditya Khamparia,Ashish Khanna,Oscar Castillo Pdf

This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.

Big Data of Complex Networks

Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl,Andreas Holzinger
Big Data of Complex Networks Book PDF

Author : Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl,Andreas Holzinger
Publisher : CRC Press
Page : 290 pages
File Size : 51,6 Mb
Release : 2016-08-19
Category : Computers
ISBN : 9781315353593

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Big Data of Complex Networks by Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl,Andreas Holzinger Pdf

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 Universität der Bundeswehr München. 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 Universität München. 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.

Data Science and Big Data Analytics in Smart Environments

Marta Chinnici,Florin Pop,Catalin Negru
Data Science and Big Data Analytics in Smart Environments Book PDF

Author : Marta Chinnici,Florin Pop,Catalin Negru
Publisher : CRC Press
Page : 305 pages
File Size : 44,8 Mb
Release : 2021-07-28
Category : Computers
ISBN : 9781000386011

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Data Science and Big Data Analytics in Smart Environments by Marta Chinnici,Florin Pop,Catalin Negru Pdf

Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment. Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.

Data Analytics, Computational Statistics, and Operations Research for Engineers

Debabrata Samanta,SK Hafizul Islam,Naveen Chilamkurti,Mohammad Hammoudeh
Data Analytics Computational Statistics and Operations Research for Engineers Book PDF

Author : Debabrata Samanta,SK Hafizul Islam,Naveen Chilamkurti,Mohammad Hammoudeh
Publisher : CRC Press
Page : 296 pages
File Size : 53,6 Mb
Release : 2022-04-05
Category : Technology & Engineering
ISBN : 9781000550429

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Data Analytics, Computational Statistics, and Operations Research for Engineers by Debabrata Samanta,SK Hafizul Islam,Naveen Chilamkurti,Mohammad Hammoudeh Pdf

With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

Statistical Learning Using Neural Networks

Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Statistical Learning Using Neural Networks Book PDF

Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Publisher : CRC Press
Page : 234 pages
File Size : 46,8 Mb
Release : 2020-09-01
Category : Business & Economics
ISBN : 9780429775550

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Statistical Learning Using Neural Networks by Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira Pdf

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Machine Learning Toolbox for Social Scientists

Yigit Aydede
Machine Learning Toolbox for Social Scientists Book PDF

Author : Yigit Aydede
Publisher : CRC Press
Page : 601 pages
File Size : 49,7 Mb
Release : 2023-09-22
Category : Computers
ISBN : 9781000958249

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Machine Learning Toolbox for Social Scientists by Yigit Aydede Pdf

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields. Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It’s self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.

Data Science with Semantic Technologies

Archana Patel,Narayan C. Debnath
Data Science with Semantic Technologies Book PDF

Author : Archana Patel,Narayan C. Debnath
Publisher : CRC Press
Page : 293 pages
File Size : 53,9 Mb
Release : 2023-06-20
Category : Computers
ISBN : 9781000881233

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Data Science with Semantic Technologies by Archana Patel,Narayan C. Debnath Pdf

As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies. Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science? This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field.

Probabilistic Foundations of Statistical Network Analysis

Harry Crane
Probabilistic Foundations of Statistical Network Analysis Book PDF

Author : Harry Crane
Publisher : CRC Press
Page : 432 pages
File Size : 45,9 Mb
Release : 2018-04-17
Category : Business & Economics
ISBN : 9781351807326

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Probabilistic Foundations of Statistical Network Analysis by Harry Crane Pdf

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Artificial Intelligence for Autonomous Networks

Mazin Gilbert
Artificial Intelligence for Autonomous Networks Book PDF

Author : Mazin Gilbert
Publisher : CRC Press
Page : 498 pages
File Size : 50,6 Mb
Release : 2018-09-25
Category : Computers
ISBN : 9781351130141

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Artificial Intelligence for Autonomous Networks by Mazin Gilbert Pdf

Artificial Intelligence for Autonomous Networks introduces the autonomous network by juxtaposing two unique technologies and communities: Networking and AI. The book reviews the technologies behind AI and software-defined network/network function virtualization, highlighting the exciting opportunities to integrate those two worlds. Outlining the new frontiers for autonomous networks, this book highlights their impact and benefits to consumers and enterprise customers. It also explores the potential of the autonomous network for transforming network operation, cyber security, enterprise services, 5G and IoT, infrastructure monitoring and traffic optimization, and finally, customer experience and care. With contributions from leading experts, this book will provide an invaluable resource for network engineers, software engineers, artificial intelligence, and machine learning researchers.

Cloud Computing Enabled Big-Data Analytics in Wireless Ad-hoc Networks

Sanjoy Das,Ram Shringar Rao,Indrani Das,Vishal Jain,Nanhay Singh
Cloud Computing Enabled Big Data Analytics in Wireless Ad hoc Networks Book PDF

Author : Sanjoy Das,Ram Shringar Rao,Indrani Das,Vishal Jain,Nanhay Singh
Publisher : CRC Press
Page : 290 pages
File Size : 41,9 Mb
Release : 2022-03-21
Category : Technology & Engineering
ISBN : 9781000539424

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Cloud Computing Enabled Big-Data Analytics in Wireless Ad-hoc Networks by Sanjoy Das,Ram Shringar Rao,Indrani Das,Vishal Jain,Nanhay Singh Pdf

This book discusses intelligent computing through the Internet of Things (IoT) and Big-Data in vehicular environments in a single volume. It covers important topics, such as topology-based routing protocols, heterogeneous wireless networks, security risks, software-defined vehicular ad-hoc networks, vehicular delay tolerant networks, and energy harvesting for WSNs using rectenna. FEATURES Covers applications of IoT in Vehicular Ad-hoc Networks (VANETs) Discusses use of machine learning and other computing techniques for enhancing performance of networks Explains game theory-based vertical handoffs in heterogeneous wireless networks Examines monitoring and surveillance of vehicles through the vehicular sensor network Investigates theoretical approaches on software-defined VANET The book is aimed at graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science, and engineering.