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Data-Driven Design and Construction

Data-Driven Design and Construction

Author: Randy Deutsch

Publisher: John Wiley & Sons

ISBN: 9781118898703

Category: Architecture

Page: 384

View: 881

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“In this comprehensive book, Professor Randy Deutsch has unlocked and laid bare the twenty-first century codice nascosto of architecture. It is data. Big data. Data as driver. . .This book offers us the chance to become informed and knowledgeable pursuers of data and the opportunities it offers to making architecture a wonderful, useful, and smart art form.” —From the Foreword by James Timberlake, FAIA Written for architects, engineers, contractors, owners, and educators, and based on today’s technology and practices, Data-Driven Design and Construction: 25 Strategies for Capturing, Applying and Analyzing Building Data addresses how innovative individuals and firms are using data to remain competitive while advancing their practices. seeks to address and rectify a gap in our learning, by explaining to architects, engineers, contractors and owners—and students of these fields—how to acquire and use data to make more informed decisions. documents how data-driven design is the new frontier of the convergence between BIM and architectural computational analyses and associated tools. is a book of adaptable strategies you and your organization can apply today to make the most of the data you have at your fingertips. Data-Driven Design and Construction was written to help design practitioners and their project teams make better use of BIM, and leverage data throughout the building lifecycle.

Data-Driven Model-Free Controllers

Data-Driven Model-Free Controllers

Author: Radu-Emil Precup

Publisher: CRC Press

ISBN: 9781000519587

Category: Technology & Engineering

Page: 402

View: 101

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This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algorithms. The second section of each chapter is dedicated to the examples and applications of the corresponding control algorithms in practical engineering problems. This book proposes to avoid complex mathematical equations, being generic as it includes several types of data-driven model-free controllers, such as Iterative Feedback Tuning controllers, Model-Free Controllers (intelligent PID controllers), Model-Free Adaptive Controllers, model-free sliding mode controllers, hybrid model‐free and model‐free adaptive‐Virtual Reference Feedback Tuning controllers, hybrid model-free and model-free adaptive fuzzy controllers and cooperative model-free controllers. The book includes the topic of optimal model-free controllers, as well. The optimal tuning of model-free controllers is treated in the chapters that deal with Iterative Feedback Tuning and Virtual Reference Feedback Tuning. Moreover, the extension of some model-free control algorithms to the consensus and formation-tracking problem of multi-agent dynamic systems is provided. This book can be considered as a textbook for undergraduate and postgraduate students, as well as a professional reference for industrial and academic researchers, attracting the readers from both industry and academia.

Data-Driven Innovation Big Data for Growth and Well-Being

Data-Driven Innovation Big Data for Growth and Well-Being

Author: OECD

Publisher: OECD Publishing

ISBN: 9789264229358

Category:

Page: 456

View: 405

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This report improves the evidence base on the role of Data Driven Innovation for promoting growth and well-being, and provide policy guidance on how to maximise the benefits of DDI and mitigate the associated economic and societal risks.

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Author: Sujit Rokka Chhetri

Publisher: Springer Nature

ISBN: 9783030379629

Category: Technology & Engineering

Page: 235

View: 710

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This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.

The Data Driven Leader

The Data Driven Leader

Author: Jenny Dearborn

Publisher: John Wiley & Sons

ISBN: 9781119382225

Category: Business & Economics

Page: 272

View: 759

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Data is your most valuable leadership asset—here's how to use it The Data Driven Leader presents a clear, accessible guide to solving important leadership challenges through human resources-focused and other data analytics. This engaging book shows you how to transform the HR function and overall organizational effectiveness by using data to make decisions grounded in facts vs. opinions, identify root causes behind your company’s thorniest problems and move toward a winning, future-focused business strategy. Realistic and actionable, this book tells the story of a successful sales executive who, after leading an analytics-driven turnaround (in Data Driven, this book’s predecessor), faces a new turnaround challenge as chief human resources officer. Each chapter features insightful commentary and practical notes on the points the story raises, guiding you to put HR analytics into action in your organization. HR and other leaders cannot afford to overlook the power and competitive advantages of data-driven decision-making and strategies. This book reflects the growing trend of CEOs choosing analytics-minded business leaders to head HR, at a time when workplaces everywhere face game-changing forces including automation, robotics and artificial intelligence. It is urgent that human resources leaders embrace analytics, not only to remain professionally relevant but also to help their organizations successfully navigate this digital transformation. HR professionals can and must: Understand essential data science principles and corporate analytics models Identify and execute effective data analytics initiatives Boost HR and company productivity and performance with metrics that matter Shape an analytics-centric culture that generates data driven leaders Most organizations capture and report data, but data is useless without analysis that leads to action. The Data Driven Leader shows you how to use this tremendous asset to lead your organization higher.

Data-Driven Decision Making and Dynamic Planning

Data-Driven Decision Making and Dynamic Planning

Author: Paul Preuss

Publisher: Routledge

ISBN: 9781317924135

Category: Education

Page: 160

View: 764

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This book will help you understand how to integrate data-based decisions into the daily work of the school. It is a practical and relevant handbook for converting data into wise decision-making and planning. It will give you the skills to successfully make data-based decisions, measure student learning and program effectiveness, evaluate student progress, use data to improve instruction, integrate a "Dynamic Planning" process into the daily operation of your school.

Data-driven travel marketing

Data-driven travel marketing

Author: Jacqueline Schmittem

Publisher: LIT Verlag Münster

ISBN: 9783643913340

Category:

Page: 87

View: 712

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A dynamic business environment, various digital marketing tools and the power of data are main challenges travel companies have to face. Up-to-dateness and flexibility are crucial for increasing competitiveness and surviving in the jungle of travel firms. But how can these challenges be managed? With a holistic view, business intelligence enhances data-driven decision-making, addresses challenges and brings firms to the next level. By combining data technologies with affiliate marketing, this book develops a data-driven concept for enhanced decision-making in affiliate travel marketing.

Data-Driven Prediction for Industrial Processes and Their Applications

Data-Driven Prediction for Industrial Processes and Their Applications

Author: Jun Zhao

Publisher: Springer

ISBN: 9783319940519

Category: Computers

Page: 443

View: 624

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This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

Creating a Data Driven System

Creating a Data Driven System

Author: Peter Holly

Publisher: Prentice Hall

ISBN: 0131723952

Category: Education

Page: 230

View: 469

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Merrill Education and ETS (Educational Testing Service) are proud to present Creatign a Data-Driven System, by Peter J. Holly (ETS2003). This brief workbook provides a model of what can be done to become a data-using system at the local level for the twin purposes of accountabilty and development. In building on current practice, this model also extrapolates from the present to the future tense by fitting the existing puzzle pieces into a more holistic, comprehensive system of date-use.

Creating Value with Big Data Analytics

Creating Value with Big Data Analytics

Author: Peter C. Verhoef

Publisher: Routledge

ISBN: 9781317561927

Category: Business & Economics

Page: 316

View: 108

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Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.

A D-Vine Copula-Based Quantile Regression Approach for the Prediction of Heating Energy Consumption. Using Historical Data for German Households

A D-Vine Copula-Based Quantile Regression Approach for the Prediction of Heating Energy Consumption. Using Historical Data for German Households

Author: Rochus Niemierko

Publisher: GRIN Verlag

ISBN: 9783346020512

Category: Business & Economics

Page: 74

View: 920

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Master's Thesis from the year 2018 in the subject Economics - Statistics and Methods, grade: 1,0, University of Augsburg, language: English, abstract: The aim of this thesis is to add to the as of yet mostly missing literature on how a D-vine copula based quantile regression model can be used to predicte the accurate level of energy consumption. Energetic retrofitting of residential buildings is poised to play an important role in the achievement of ambitious global climate targets. A prerequisite for purposeful policy-making and private investments is the accurate prediction of energy consumption. Building energy models are mostly based on engineering methods quantifying theoretical energy consumption. However, a performance gap between predicted and actual consumption has been identified in literature. Data- driven methods using historical data can potentially overcome this issue. The D-vine copula-based quantile regression model used in this study achieved very good fitting results based on a representative data set comprising 25,000 German households. The findings suggest that quantile regression increases transparency by analyzing the entire distribution of heating energy consumption for individual building characteristics. More specifically, the analyses reveal the following exemplary insights. First, for different levels of energy efficiency, the rebound effect exhibits cyclical behavior and significantly varies across quantiles. Second, very energy-conscious and energy-wasteful households are prone to more extreme rebound effects. Third, with regards to the performance gap, heating energy demand of inefficient buildings is systematically underestimated, while it is overestimated for efficient buildings. Therefore, The remainder of this thesis is organized as follows. Section 2 presents a concise categorization of building energy models. Section 3 presents existing data-driven methods used for the pre-diction of heating energy consumption in the residential sector. Next, Section 4 elaborates on vine copula-based quantile regression. This is followed by a description of the data employed in Section 5. Section 6 presents the empirical results and Section 7 provides the practical im-plications and contribution of the quantile regression approach introduced. Finally, the conclu-sions and limitations of this thesis are discussed in Section 8.