Data Science In Engineering And Management

Author : Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev
Publisher : CRC Press
Page : 159 pages
File Size : 50,6 Mb
Release : 2021-12-31
Category : Technology & Engineering
ISBN : 9781000520842

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Data Science in Engineering and Management by Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev Pdf

This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis. Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively. This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

Author : Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev
Publisher : CRC Press
Page : 161 pages
File Size : 44,7 Mb
Release : 2021-12-30
Category : Business & Economics
ISBN : 9781000520774

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Data Science in Engineering and Management by Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev Pdf

This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis. Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively. This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

Author : Ivan Damnjanovic,Kenneth Reinschmidt
Publisher : Springer
Page : 379 pages
File Size : 54,6 Mb
Release : 2019-05-23
Category : Technology & Engineering
ISBN : 9783030142513

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Data Analytics for Engineering and Construction Project Risk Management by Ivan Damnjanovic,Kenneth Reinschmidt Pdf

This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.

Author : Rajesh Jugulum
Publisher : CRC Press
Page : 126 pages
File Size : 53,7 Mb
Release : 2018-09-03
Category : Business & Economics
ISBN : 9780429877261

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Robust Quality by Rajesh Jugulum Pdf

Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution

Author : Fausto Pedro García Márquez,Benjamin Lev
Publisher : Springer
Page : 316 pages
File Size : 45,7 Mb
Release : 2019-01-04
Category : Business & Economics
ISBN : 9783319956510

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Data Science and Digital Business by Fausto Pedro García Márquez,Benjamin Lev Pdf

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

Author : Fausto Pedro García Márquez,Isaac Segovia Ramírez,Pedro José Bernalte Sánchez,Alba Muñoz del Río
Publisher : Springer Nature
Page : 551 pages
File Size : 43,7 Mb
Release : 2023-03-24
Category : Technology & Engineering
ISBN : 9783031279157

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IoT and Data Science in Engineering Management by Fausto Pedro García Márquez,Isaac Segovia Ramírez,Pedro José Bernalte Sánchez,Alba Muñoz del Río Pdf

This book presents the selected research works from the 16th International Conference on Industrial Engineering and Industrial Management in 2022. The conference was promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), organized by Ingenium Research Group at Universidad de Castilla-La Mancha, Spain, and it took place on July 7th and 8th, 2022, in Toledo, Spain. The book highlights some of the latest research advances and cutting-edge analyses of real-world case studies on Industrial Engineering and Industrial Management from a wide range of international contexts. It also identifies business applications and the latest findings and innovations in Operations Management and in Decision Sciences.

Author : Kirill Dubovikov
Publisher : Packt Publishing Ltd
Page : 276 pages
File Size : 54,6 Mb
Release : 2019-11-12
Category : Computers
ISBN : 9781838824563

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Managing Data Science by Kirill Dubovikov Pdf

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Author : Jorge Marx Gómez,Manuel Mora,Mahesh S. Raisinghani,Wolfgang Nebel,Rory V. O'Connor
Publisher : Springer
Page : 290 pages
File Size : 41,9 Mb
Release : 2017-11-10
Category : Computers
ISBN : 9783319650821

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Engineering and Management of Data Centers by Jorge Marx Gómez,Manuel Mora,Mahesh S. Raisinghani,Wolfgang Nebel,Rory V. O'Connor Pdf

This edited volume covers essential and recent development in the engineering and management of data centers. Data centers are complex systems requiring ongoing support, and their high value for keeping business continuity operations is crucial. The book presents core topics on the planning, design, implementation, operation and control, and sustainability of a data center from a didactical and practitioner viewpoint. Chapters include: · Foundations of data centers: Key Concepts and Taxonomies · ITSDM: A Methodology for IT Services Design · Managing Risks on Data Centers through Dashboards · Risk Analysis in Data Center Disaster Recovery Plans · Best practices in Data Center Management Case: KIO Networks · QoS in NaaS (Network as a Service) using Software Defined Networking · Optimization of Data Center Fault-Tolerance Design · Energetic Data Centre Design Considering Energy Efficiency Improvements During Operation · Demand-side Flexibility and Supply-side Management: The Use Case of Data Centers and Energy Utilities · DevOps: Foundations and its Utilization in Data Centers · Sustainable and Resilient Network Infrastructure Design for Cloud Data Centres · Application Software in Cloud-Ready Data Centers This book bridges the gap between academia and the industry, offering essential reading for practitioners in data centers, researchers in the area, and faculty teaching related courses on data centers. The book can be used as a complementary text for traditional courses on Computer Networks, as well as innovative courses on IT Architecture, IT Service Management, IT Operations, and Data Centers.

Author : Atzmueller, Martin
Publisher : IGI Global
Page : 272 pages
File Size : 50,7 Mb
Release : 2016-06-01
Category : Computers
ISBN : 9781522502944

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Enterprise Big Data Engineering, Analytics, and Management by Atzmueller, Martin Pdf

The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.

Author : Songmao Zhang,Martin Wirsing,Zili Zhang
Publisher : Springer
Page : 858 pages
File Size : 47,5 Mb
Release : 2015-10-23
Category : Computers
ISBN : 9783319251592

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Knowledge Science, Engineering and Management by Songmao Zhang,Martin Wirsing,Zili Zhang Pdf

This book constitutes the refereed proceedings of the 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015, held in Chongqing, China, in October 2015. The 57 revised full papers presented together with 22 short papers and 5 keynotes were carefully selected and reviewed from 247 submissions. The papers are organized in topical sections on formal reasoning and ontologies; knowledge management and concept analysis; knowledge discovery and recognition methods; text mining and analysis; recommendation algorithms and systems; machine learning algorithms; detection methods and analysis; classification and clustering; mobile data analytics and knowledge management; bioinformatics and computational biology; and evidence theory and its application.

Author : Christos Douligeris,Dimitris Karagiannis,Dimitris Apostolou
Publisher : Springer Nature
Page : 868 pages
File Size : 45,6 Mb
Release : 2019-08-20
Category : Computers
ISBN : 9783030295516

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Knowledge Science, Engineering and Management by Christos Douligeris,Dimitris Karagiannis,Dimitris Apostolou Pdf

This two-volume set of LNAI 11775 and LNAI 11776 constitutes the refereed proceedings of the 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, held in Athens, Greece, in August 2019. The 77 revised full papers and 23 short papers presented together with 10 poster papers were carefully reviewed and selected from 240 submissions. The papers of the first volume are organized in the following topical sections: Formal Reasoning and Ontologies; Recommendation Algorithms and Systems; Social Knowledge Analysis and Management ; Data Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge Engineering Applications. The papers of the second volume are organized in the following topical sections: Probabilistic Models and Applications; Text Mining and Document Analysis; Knowledge Theories and Models; and Network Knowledge Representation and Learning.

Author : Zhi Jin
Publisher : Springer Nature
Page : 456 pages
File Size : 43,9 Mb
Release : 2024-06-07
Category : Electronic
ISBN : 9783031402890

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Knowledge Science, Engineering and Management by Zhi Jin Pdf

Author : Tim Menzies,Laurie Williams,Thomas Zimmermann
Publisher : Morgan Kaufmann
Page : 408 pages
File Size : 50,6 Mb
Release : 2016-07-14
Category : Computers
ISBN : 9780128042618

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Perspectives on Data Science for Software Engineering by Tim Menzies,Laurie Williams,Thomas Zimmermann Pdf

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

Author : Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publisher : Academic Press
Page : 320 pages
File Size : 43,7 Mb
Release : 2019-11-13
Category : Science
ISBN : 9780128183199

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Handbook of Data Science Approaches for Biomedical Engineering by Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari Pdf

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Author : Gang Li,Heng Tao Shen,Ye Yuan,Xiaoyang Wang,Huawen Liu,Xiang Zhao
Publisher : Springer Nature
Page : 495 pages
File Size : 51,6 Mb
Release : 2020-08-20
Category : Computers
ISBN : 9783030553937

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Knowledge Science, Engineering and Management by Gang Li,Heng Tao Shen,Ye Yuan,Xiaoyang Wang,Huawen Liu,Xiang Zhao Pdf

This two-volume set of LNAI 12274 and LNAI 12275 constitutes the refereed proceedings of the 13th International Conference on Knowledge Science, Engineering and Management, KSEM 2020, held in Hangzhou, China, in August 2020.* The 58 revised full papers and 27 short papers were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: knowledge graph; knowledge representation; knowledge management for education; knowledge-based systems; and data processing and mining. The papers of the second volume are organized in the following topical sections: machine learning; recommendation algorithms and systems; social knowledge analysis and management; text mining and document analysis; and deep learning. *The conference was held virtually due to the COVID-19 pandemic.