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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Theory and Practical Applications

by Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi

  • Publisher : Elsevier
  • Release : 2020-07-03
  • Pages : 328
  • ISBN : 0128193662
  • Language : En, Es, Fr & De
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
A Book

by Chris Aldrich,Lidia Auret

  • Publisher : Springer Science & Business Media
  • Release : 2013-06-15
  • Pages : 374
  • ISBN : 1447151852
  • Language : En, Es, Fr & De
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This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Financial Signal Processing and Machine Learning

Financial Signal Processing and Machine Learning
A Book

by Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov

  • Publisher : John Wiley & Sons
  • Release : 2016-05-31
  • Pages : 320
  • ISBN : 1118745671
  • Language : En, Es, Fr & De
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The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

The International Journal, Advanced Manufacturing Technology

The International Journal, Advanced Manufacturing Technology
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1987
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Mastering Scala Machine Learning

Mastering Scala Machine Learning
A Book

by Alex Kozlov

  • Publisher : Packt Publishing Ltd
  • Release : 2016-06-28
  • Pages : 310
  • ISBN : 178588526X
  • Language : En, Es, Fr & De
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Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop About This Book This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability Who This Book Is For Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required. What You Will Learn Sharpen your functional programming skills in Scala using REPL Apply standard and advanced machine learning techniques using Scala Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala. Style and approach This hands-on guide dives straight into implementing Scala for machine learning without delving much into mathematical proofs or validations. There are ample code examples and tricks that will help you sail through using the standard techniques and libraries. This book provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

Data Driven Decision Making Under Uncertainty for Intelligent Life-cycle Control of the Built Environment

Data Driven Decision Making Under Uncertainty for Intelligent Life-cycle Control of the Built Environment
A Book

by Charalampos Andriotis

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This dissertation provides novel frameworks for data-driven probabilistic performance-based assessments and optimal or near-optimal stochastic control strategies for structural, infrastructural and other engineering systems. The goal of this research is to enable efficient and robust structural performance predictions and optimized decisions over the entire operating life of systems, by developing advanced statistical learning models, machine learning formulations and Artificial Intelligence (AI) algorithms, in order to contribute to a future of smart and sustainable infrastructure. To this end, the developed approaches build upon and extend well-established statistical modeling frameworks, infuse intelligence to structural informatics through newly introduced schemes for structural data mining and processing, provide comprehensive solutions to challenging life-cycle objectives, and support complex decisions in previously intractable sequential decision-making problems through novel AI-aided algorithms and theoretical concepts.Efficient assessment of various societal, environmental and economic losses necessitates adept statistical and learning models, able to consistently capture longitudinal dependencies in data and translate multivariate information in structural condition and performance metrics. This dissertation addresses this need, within a softmax regression fragility analysis framework that avoids fragility function crossing inconsistencies and scales well in high-dimensional intensity measure spaces with multiple structural states. Moreover, softmax-based fragility functions are generalized by advanced statistical learning and deep learning formulations that employ Dynamic Bayesian Networks (DBNs), in the form of Dependent Markov Models (DMMs) and Dependent Hidden Markov Models (DHMMs), as well as Recurrent Neural Network (RNN) architectures. The above considerably extend and generalize the framework of probabilistic performance engineering, with theoretically consistent multi-state multi-variate fragility functions, which also have multi-step predictive capabilities in time. The hidden spaces of DHMMs and RNNs are shown to be able to encode noisy input to noisy output sequences through structured hidden spaces. It turns out that the Markovian properties of these spaces can portray damage-consistent dynamics, whereas they are directly pertinent to the input required in advanced decision frameworks that employ Markovian processes for decision-making either under full, partial, or mixed observability assumptions.Hidden Markov models equipped with costs and control actions can provide a theoretically neat and computationally robust framework for sequential decision-making problems under uncertainty, through Partially Observable Markov Decision Processes (POMDPs). This research casts stochastic control problems for determination of optimal or near-optimal life-cycle maintenance and inspection strategies within the premises of POMDPs. Specialized formulations of full or mixed observability are also developed, through Markov Decision Processes (MDPs) or Mixed Observability Markov Decision Processes (MOMDPs), respectively. Along these lines, this research enables decision-support systems which can operate in stochastic engineering environments with uncertain action outcomes and noisy real-time observations, having global optimality guarantees as a result of the relevant underlying dynamic programming formulations introduced and, in many cases, well-defined performance bounds. In the same vein, the Value of Information (VoI) and the Value of Structural Health Monitoring (VoSHM) are quantified and a straightforward definition for the expected life-cycle gains of different observational and monitoring options is established and evaluated. Formulating VoI and VoSHM within the framework of POMDPs, the estimates of these metrics depict value gaps between the optimal life-cycle strategies of the examined options, thus also being able to provide bounds on the respective gains.For small- to medium-scale systems, solutions to the life-cycle optimization problems are derived by point-based solution schemes which provide efficient exploration heuristics, value function updates over the POMDP belief-space, vector compression techniques and convergence properties. For large-scale multi-component engineering systems that form large state and action spaces, such point-based schemes are however impractical as they require explicit prior information of the system dynamics model. To this end, the Deep Centralized Multi-agent Actor Critic (DCMAC) is developed herein and implemented in the solution procedure. DCMAC is an efficient off-policy actor-critic Deep Reinforcement Learning (DRL) algorithm with experience replay. DCMAC alleviates the curse of dimensionality related to state, observation and actions spaces of multi-component systems through deep network approximators and a factorized representation of the actor. DCMAC interacts directly with the simulator, thus avoiding the need for full and explicit model-based knowledge of the system dynamics, and operates in the POMDP belief space, by encoding sequences of actions and observations in belief vectors through Bayesian updates. Overall, DCMAC is able to efficiently tackle the state and action space scalability issues, as well as the potential model unavailability at the system level, all of which often make the decision problems of large multi-component systems hard to solve, if not intractable, by conventional machine learning schemes and other life-cycle optimization methodologies.All developed methods and frameworks are rigorously evaluated in relevant numerical applications and their strengths, limitations and broader capabilities are highlighted and discussed. Results demonstrate the effectiveness of the proposed models, solution procedures and algorithmic schemes, in enabling efficient data-driven probabilistic predictions and structural informatics, as well as comprehensive optimal or near-optimal stochastic control strategies for engineering systems. Overall, the originally developed statistical and machine learning models, in conjunction with the dedicated AI-aided algorithms, can ensure advanced and sophisticated solutions and open numerous new scientific paths towards smart cities, intelligent infrastructure, and autonomous control of the built environment.

Big Data and Networks Technologies

Big Data and Networks Technologies
A Book

by Yousef Farhaoui

  • Publisher : Springer
  • Release : 2019-07-17
  • Pages : 372
  • ISBN : 3030236722
  • Language : En, Es, Fr & De
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This book reviews the state of the art in big data analysis and networks technologies. It addresses a range of issues that pertain to: signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart cities, networks technologies, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. In turn, data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and the social sciences. All papers presented here are the product of extensive field research involving applications and techniques related to data analysis in general, and to big data and networks technologies in particular. Given its scope, the book will appeal to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well general readers interested in big data analysis and networks technologies.

Emerging Technologies in Data Mining and Information Security

Emerging Technologies in Data Mining and Information Security
Proceedings of IEMIS 2018

by Ajith Abraham,Paramartha Dutta,Jyotsna Kumar Mandal,Abhishek Bhattacharya,Soumi Dutta

  • Publisher : Springer
  • Release : 2018-12-12
  • Pages : 861
  • ISBN : 9811319510
  • Language : En, Es, Fr & De
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This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2018) held at the University of Engineering & Management, Kolkata, India, on February 23–25, 2018. It comprises high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT) and information security.

Intelligent Information and Database Systems

Intelligent Information and Database Systems
12th Asian Conference, ACIIDS 2020, Phuket, Thailand, March 23–26, 2020, Proceedings

by Paweł Sitek

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 9811533806
  • Language : En, Es, Fr & De
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Electrical & Electronics Abstracts

Electrical & Electronics Abstracts
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1997
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Interdisciplinary Evolution of the Machine Brain

Interdisciplinary Evolution of the Machine Brain
Vision, Touch & Mind

by Wenfeng Wang,Hengjin Cai,Xiangyang Deng,Chenguang Lu,Limin Zhang

  • Publisher : Springer
  • Release : 2021-02-05
  • Pages : 145
  • ISBN : 9789813342439
  • Language : En, Es, Fr & De
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This book seeks to interpret connections between the machine brain, mind and vision in an alternative way and promote future research into the Interdisciplinary Evolution of Machine Brain (IEMB). It gathers novel research on IEMB, and offers readers a step-by-step introduction to the theory and algorithms involved, including data-driven approaches in machine learning, monitoring and understanding visual environments, using process-based perception to expand insights, mechanical manufacturing for remote sensing, reconciled connections between the machine brain, mind and vision, and the interdisciplinary evolution of machine intelligence. This book is intended for researchers, graduate students and engineers in the fields of robotics, Artificial Intelligence and brain science, as well as anyone who wishes to learn the core theory, principles, methods, algorithms, and applications of IEMB.

Proceedings of International Conference on Sustainable Expert Systems

Proceedings of International Conference on Sustainable Expert Systems
ICSES 2020

by Subarna Shakya,Valentina Emilia Balas,Wang Haoxiang,Zubair Baig

  • Publisher : Springer Nature
  • Release : 2021-05-01
  • Pages : 715
  • ISBN : 9813343559
  • Language : En, Es, Fr & De
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This book includes papers on intelligent expert systems and sustainability applications in the areas of data science, image processing, wireless communication, risk assessment, healthcare, intelligent social network mining, and energy. The recent growth of sustainability leads to a progressively new era of computing, where its design and deployment leverages significant impact on the intelligent systems research. Moreover, the sustainability technologies can be effectively used in the progressive deployment of various network-enabled technologies like intelligent sensors, smart cities, wearable technologies, robotics, web applications and other such Internet technologies. The thrust of this book is to publish the state-of-the-art research articles that deals with the design, development, implementation and testing of the intelligent expert systems and also to provide an overview of the sustainable management of these systems.

Intelligent and Fuzzy Techniques: Smart and Innovative Solutions

Intelligent and Fuzzy Techniques: Smart and Innovative Solutions
Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23, 2020

by Cengiz Kahraman,Sezi Cevik Onar,Basar Oztaysi,Irem Ucal Sari,Selcuk Cebi,A. Cagri Tolga

  • Publisher : Springer Nature
  • Release : 2020-07-10
  • Pages : 1705
  • ISBN : 3030511561
  • Language : En, Es, Fr & De
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This book gathers the most recent developments in fuzzy & intelligence systems and real complex systems presented at INFUS 2020, held in Istanbul on July 21–23, 2020. The INFUS conferences are a well-established international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing, highlighting studies on fuzzy & intelligence systems and real complex systems at universities and international research institutions. Covering a range of topics, including the theory and applications of fuzzy set extensions such as intuitionistic fuzzy sets, hesitant fuzzy sets, spherical fuzzy sets, and fuzzy decision-making; machine learning; risk assessment; heuristics; and clustering, the book is a valuable resource for academics, M.Sc. and Ph.D. students, as well as managers and engineers in industry and the service sectors.

15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)

15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)
A Book

by Álvaro Herrero,Carlos Cambra,Daniel Urda,Javier Sedano,Héctor Quintián,Emilio Corchado

  • Publisher : Springer Nature
  • Release : 2020-08-28
  • Pages : 876
  • ISBN : 303057802X
  • Language : En, Es, Fr & De
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This book contains accepted papers presented at SOCO 2020 conference held in the beautiful and historic city of Burgos (Spain), in September 2020. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a through peer-review process, the SOCO 2020 International Program Committee selected 83 papers which are published in these conference proceedings and represents an acceptance rate of 35%. Due to the COVID-19 outbreak, the SOCO 2020 edition was blended, combining on-site and on-line participation. In this relevant edition a special emphasis was put on the organization of special sessions. Eleven special session were organized related to relevant topics such as: Soft Computing Applications in Precision Agriculture, Manufacturing and Management Systems, Management of Industrial and Environmental Enterprises, Logistics and Transportation Systems, Robotics and Autonomous Vehicles, Computer Vision, Laser-Based Sensing and Measurement and other topics such as Forecasting Industrial Time Series, IoT, Big Data and Cyber Physical Systems, Non-linear Dynamical Systems and Fluid Dynamics, Modeling and Control systems The selection of papers was extremely rigorous in order to maintain the high quality of SOCO conference editions and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the SOCO conference would not exist without their help.

General Catalog -- University of California, Santa Cruz

General Catalog -- University of California, Santa Cruz
A Book

by University of California, Santa Cruz

  • Publisher : Unknown Publisher
  • Release : 2006
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Information Technology - New Generations

Information Technology - New Generations
14th International Conference on Information Technology

by Shahram Latifi

  • Publisher : Springer
  • Release : 2017-08-26
  • Pages : 985
  • ISBN : 3319549782
  • Language : En, Es, Fr & De
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This volume presents a collection of peer-reviewed, scientific articles from the 14th International Conference on Information Technology - New Generations, held at Tuscany Suites Hotel in Las Vegas. The proceedings addresses critical areas of information technology including web technology, communications, computing architectures, software engineering, security, and data mining.

22nd European Symposium on Computer Aided Process Engineering

22nd European Symposium on Computer Aided Process Engineering
A Book

by Anonim

  • Publisher : Elsevier
  • Release : 2012-12-10
  • Pages : 1400
  • ISBN : 0444594566
  • Language : En, Es, Fr & De
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Computer aided process engineering (CAPE) plays a key design and operations role in the process industries. This conference features presentations by CAPE specialists and addresses strategic planning, supply chain issues and the increasingly important area of sustainability audits. Experts collectively highlight the need for CAPE practitioners to embrace the three components of sustainable development: environmental, social and economic progress and the role of systematic and sophisticated CAPE tools in delivering these goals. Contributions from the international community of researchers and engineers using computing-based methods in process engineering Review of the latest developments in process systems engineering Emphasis on a systems approach in tackling industrial and societal grand challenges

International Aerospace Abstracts

International Aerospace Abstracts
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Big Data Analytics for Time-Critical Mobility Forecasting

Big Data Analytics for Time-Critical Mobility Forecasting
From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains

by George A. Vouros,Gennady Andrienko,Christos Doulkeridis,Nikolaos Pelekis,Alexander Artikis,Anne-Laure Jousselme,Cyril Ray,Jose Manuel Cordero,David Scarlatti

  • Publisher : Springer Nature
  • Release : 2020-06-23
  • Pages : 361
  • ISBN : 303045164X
  • Language : En, Es, Fr & De
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This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities’ characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains.

American Doctoral Dissertations

American Doctoral Dissertations
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2000
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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