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| data science and management impact factor: Advances in Data Science and Management Samarjeet Borah, Valentina Emilia Balas, Zdzislaw Polkowski, 2020-01-13 This book includes high-quality papers presented at the International Conference on Data Science and Management (ICDSM 2019), organised by the Gandhi Institute for Education and Technology, Bhubaneswar, from 22 to 23 February 2019. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands. |
| data science and management impact factor: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science and management impact factor: Domain Driven Data Mining Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao, 2010-01-08 This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output. |
| data science and management impact factor: Intelligent Data Analysis Michael R. Berthold, David J Hand, 2007-06-07 This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes. |
| data science and management impact factor: Journal of Database Management ( Vol 23 ISS 1) Keng Siau, 2011-12 |
| data science and management impact factor: TinyML Pete Warden, Daniel Situnayake, 2019-12-16 Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size |
| data science and management impact factor: International Journal of Business Analytics (IJBAN). John Wang, 2015 |
| data science and management impact factor: Data Management, Analytics and Innovation Saptarsi Goswami, Inderjit Singh Barara, Amol Goje, C. Mohan, Alfred M. Bruckstein, 2022-09-21 This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Sixth International Conference on Data Management, Analytics and Innovation (ICDMAI 2022), held virtually during January 14–16, 2022. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| data science and management impact factor: Artificial Intelligence, Machine Learning, and Data Science Technologies Neeraj Mohan, Ruchi Singla, Priyanka Kaushal, Seifedine Kadry, 2021-10-11 This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science. |
| data science and management impact factor: Large Scale and Big Data Sherif Sakr, Mohamed Gaber, 2014-06-25 Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques. |
| data science and management impact factor: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 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. |
| data science and management impact factor: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science and management impact factor: International Journal of Knowledge Management , 2013 |
| data science and management impact factor: Ecological Informatics Friedrich Recknagel, 2002-12-11 Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery. |
| data science and management impact factor: Concise Survey of Computer Methods Peter Naur, 1974 |
| data science and management impact factor: Advances in Computing and Data Sciences Mayank Singh, P. K. Gupta, Vipin Tyagi, Jan Flusser, Tuncer Ören, Gianluca Valentino, 2020-07-17 This book constitutes the post-conference proceedings of the 4th International Conference on Advances in Computing and Data Sciences, ICACDS 2020, held in Valletta, Malta, in April 2020.* The 46 full papers were carefully reviewed and selected from 354 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations. * The conference was held virtually due to the COVID-19 pandemic. |
| data science and management impact factor: Discriminatory Pricing of Over-the-Counter Derivatives Hau Harald, Peter Hoffmann, Sam Langfield, Mr.Yannick Timmer, 2019-05-07 New regulatory data reveal extensive price discrimination against non-financial clients in the FX derivatives market. The client at the 90th percentile pays an effective spread of 0.5%, while the bottom quarter incur transaction costs of less than 0.02%. Consistent with models of search frictions in over-the-counter markets, dealers charge higher spreads to less sophisticated clients. However, price discrimination is eliminated when clients trade through multi-dealer request-for-quote platforms. We also document that dealers extract rents from captive clients and market opacity, but only for contracts negotiated bilaterally with unsophisticated clients. |
| data science and management impact factor: Innovations in Classification, Data Science, and Information Systems Daniel Baier, Klaus-Dieter Wernecke, 2006-06-06 The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques. |
| data science and management impact factor: Data Management, Analytics and Innovation Neha Sharma, Amlan Chakrabarti, Valentina Emilia Balas, Alfred M. Bruckstein, 2021-09-19 This book presents the latest findings in the areas of data management and smart computing, machine learning, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Fifth International Conference on Data Management, Analytics and Innovation (ICDMAI 2021), held during January 15–17, 2021, in a virtual mode. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| data science and management impact factor: Handbook of Data Science Approaches for Biomedical Engineering Valentina Emilia Balas, Vijender Kumar Solanki, Manju Khari, Raghvendra Kumar, 2019-11-13 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 |
| data science and management impact factor: Management Decision-Making, Big Data and Analytics Simone Gressel, David J. Pauleen, Nazim Taskin, 2020-10-12 Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels. |
| data science and management impact factor: Cybersecurity Management in Education Technologies Ahmed A. Abd El-Latif, Yassine Maleh, Mohammed A. El-Affendi, Sadique Ahmad, 2023-12-06 This book explores the intersection of cybersecurity and education technologies, providing practical solutions, detection techniques, and mitigation strategies to ensure a secure and protected learning environment in the face of evolving cyber threats. With a wide range of contributors covering topics from immersive learning to phishing detection, this book is a valuable resource for professionals, researchers, educators, students, and policymakers interested in the future of cybersecurity in education. Features: • Offers both theoretical foundations and practical guidance for fostering a secure and protected environment for educational advancements in the digital age. • Addresses the need for cybersecurity in education in the context of worldwide changes in education sources and advancements in technology. • Highlights the significance of integrating cybersecurity into educational practices and protecting sensitive information to ensure students’ performance prediction systems are not misused. • Covers a wide range of topics including immersive learning, cybersecurity education, and malware detection, making it a valuable resource for professionals, researchers, educators, students, and policymakers. |
| data science and management impact factor: Maintenance Management Fausto Pedro García Márquez, Mayorkinos Papaelias, 2020-01-22 Maintenance is a critical variable in industry to achieve competitiveness. Therefore, correct management of corrective, predictive, and preventive politics in any industry is required. Maintenance Management considers the main concepts, state of the art, advances, and case studies in this topic. This book complements other subdisciplines such as economics, finance, marketing, decision and risk analysis, engineering, etc.The book analyzes real case studies in multiple disciplines. It considers the topics of failure detection and diagnosis, fault trees, and subdisciplines (e.g. FMECA, FMEA, etc.). It is essential to link these topics with finance, scheduling, resources, downtime, etc. to increase productivity, profitability, maintainability, reliability, safety, and availability, and reduce costs and downtime.This book presents important advances in mathematics, models, computational techniques, dynamic analysis, etc., which are all employed in maintenance management.Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support the analysis of multicriteria decision-making problems with defined constraints and requirements.The book is ideal for graduate students and professionals in industrial engineering, business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying maintenance or who are required to solve large, specific, and complex maintenance management problems as part of their jobs. The book will also be of interest to researchers from academia. |
| data science and management impact factor: Big Data Analytics Arun K. Somani, Ganesh Chandra Deka, 2017-10-30 The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers. |
| data science and management impact factor: Disaster Prevention Ilan Kelman, 2015-11-19 Leading editors have curated collections of important Routledge research in ebook form to share recommended paths to understanding cutting-edge topics. In this book Ilan Kelman presents his guide to the must-read research on the subject of Disaster Prevention. |
| data science and management impact factor: Big Data Analytics and Knowledge Discovery Min Song, Il-Yeol Song, Gabriele Kotsis, A Min Tjoa, Ismail Khalil, 2020-09-10 The volume LNCS 12393 constitutes the papers of the 22nd International Conference Big Data Analytics and Knowledge Discovery which will be held online in September 2020. The 15 full papers presented together with 14 short papers plus 1 position paper in this volume were carefully reviewed and selected from a total of 77 submissions. This volume offers a wide range to following subjects on theoretical and practical aspects of big data analytics and knowledge discovery as a new generation of big data repository, data pre-processing, data mining, text mining, sequences, graph mining, and parallel processing. |
| data science and management impact factor: Data Science and Artificial Intelligence for Digital Healthcare Pradeep Kumar Singh, |
| data science and management impact factor: Advances in Data Science and Information Engineering Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Cheng-Ying Yang, Hamid R. Arabnia, Leonidas Deligiannidis, 2021-10-29 The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students. |
| data science and management impact factor: COVID-19: Integrating Artificial Intelligence, Data Science, Mathematics, Medicine and Public Health, Epidemiology, Neuroscience, Neurorobotics, and Biomedical Science in Pandemic Management, volume II Atefeh Abedini, Reza Lashgari, 2024-02-29 |
| data science and management impact factor: Research Challenges in Information Science Fabiano Dalpiaz, Jelena Zdravkovic, Pericles Loucopoulos, 2020-06-24 This book constitutes the proceedings of the 14th International Conference on Research Challenges in Information Sciences, RCIS 2020, held in Limassol, Cyprus, during September 23-25, 2020. The conference was originally scheduled for May 2020, but the organizing committee was forced to postpone the conference due to the outbreak of the COVID-19 pandemic. The scope of RCIS 2020 is summarized by the thematic areas of information systems and their engineering; user-oriented approaches; data and information management; business process management; domain-specific information systems engineering; data science; information infrastructures, and reflective research and practice. The 26 full papers and 3 work in progress papers presented in this volume were carefully reviewed and selected from 106 submissions. They were organized in topical sections named: Data Analytics and Business Intelligence; Digital Enterprise and Technologies; Human Factors in Information Systems; Information Systems Development and Testing; Machine Learning and Text Processing; and Security and Privacy. The volume also contains 12 poster and demo-papers, and 4 Doctoral Consortium papers. |
| data science and management impact factor: Data Science Advancements in Pandemic and Outbreak Management Asimakopoulou, Eleana, Bessis, Nik, 2021-04-09 Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks. |
| data science and management impact factor: Data Analytics for Supply Chain Networks Niamat Ullah Ibne Hossain, 2023-06-22 The objective of the book is to adopt the application of data analytics to enhance the sustainability and resilience of the green supply chain networks. To demonstrate the applicability and usefulness of the method, the book adopts different data analytic models and approaches against the backdrop of case studies. In summary, this book attempts to address the question of methods, tools, and techniques that can be used to create resilient, anti-fragile, reliable, and invulnerable green supply chain networks. |
| data science and management impact factor: New Approaches to Data Analytics and Internet of Things Through Digital Twin Karthikeyan, P., Katina, Polinpapilinho F., Anandaraj, S.P., 2022-09-30 Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students. |
| data science and management impact factor: International Conference on Artificial Intelligence and Sustainable Engineering Goutam Sanyal, Carlos M. Travieso-González, Shashank Awasthi, Carla M.A. Pinto, B. R. Purushothama, 2022-04-07 This book comprises select papers from the International Conference on Artificial Intelligence and Sustainable Engineering (AISE 2020). The volume focuses on the recent advancements in artificial intelligence and addresses how it is useful in achieving truly sustainable solutions. The key strands of this book include artificial intelligence in healthcare, IoT for modern life, security and surveillance, big data analytics, machine learning and computing, communication technologies, gesture technology, virtual intelligence, and audio & speech processing. The book addresses sustainability challenges in various computing techniques and opportunities for sustainable engineering based on AI and supporting tools such as engineering design for sustainable development using IoT/AI, smart cities: waste minimization, remanufacturing, reuse and recycling technologies using IoT/AI, industry 4.0, intelligent and smart grid systems, energy conservation using technology, green engineering/technology, robotic process automation (RPA) and water and air quality management. This book can be a valuable resource for academicians, researchers, and professionals working in AI and its applications. |
| data science and management impact factor: Social Media for Project Management Johan Ninan, 2022-11-17 The number of projects is increasing worldwide as traditional and repetitive tasks are carried out through automation. Projects being temporary and unique while being adopted globally across sectors presents a challenge for the effective management of environmental, economic, and social parameters. Projects are people centric and require the effective management of internal and external stakeholders. In the modern age, social media is seen as a tool that connects people across the world having significant implications on everyone’s daily life. Social media is used for different purposes and encompasses multiple affordances as these are often free and also bring together people from different walks of life who tend to use them differently. However, the role of social media in managing projects is still under explored. In this edited book, multiple authors working on the application of social media in projects come together to craft an agenda for the future. First, the use of social media for internal stakeholders, such as managers and engineers, are discussed. Following this, the use of social media for external stakeholders, such as communities and project affected persons are discussed. Finally, the guidelines for education using social media and research using social media is discussed. Thus, the book brings together multiple authors to discuss how social media can be used in project settings to facilitate interactions and strategic conversations across hierarchical levels and geographic boundaries for diverse goals. The book is a valuable resource for all project management academics, researchers and practitioners who are interested in learning about the application of social media in project settings. |
| data science and management impact factor: Economics & Management 3/2024 AA.VV., 2024-10-07T00:00:00+02:00 While demographic change may seem slow and unnoticeable, its impact on the economic, social, and political future of a country is profound. This is particularly evident in Italy, the third oldest country in the world after Monaco and Japan. Growing longevity and the coexistence of multiple generations within families, organizations, and markets require a rethinking of education and integration policies. Embracing a demographic perspective can facilitate the development of innovative public policies and sustainable business strategies. |
| data science and management impact factor: GIS, Applied Computing and Data Science for Water Management Noamen Rebai, |
| data science and management impact factor: Blockchain, IoT, and AI Technologies for Supply Chain Management Priyanka Chawla, Adarsh Kumar, Anand Nayyar, Mohd Naved, 2023-05-05 Supply chain management, often known as SCM, refers to the extensive variety of operations that are required to plan, monitor, and coordinate the movement of a product from its raw materials to its finished state in the most time- and money-efficient manner possible. How the supply chain is managed has an impact not only on the quality of the product and the service but also on the distribution, costs, and overall customer experience. Supply chain management is a massive undertaking that needs firms to reevaluate the method in which they operate their supply chains. Blockchain, IoT, and AI Technologies for Supply Chain Management discusses the problems and difficulties that the facilitators of the supply chain confront, in addition to the possible solutions to such problems and difficulties. This book will be the only one of its kind to address the impact of COVID-19 on supply chain systems involving different stakeholders such as producers, dealers, and manufacturers and will provide a foundation for future research opportunities that will allow for the unrestricted expansion and prosperity of business. It will serve as a foundation for academics, scientists, and educationists interested in the use of modern technologies in the field of supply chain management, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain. In addition to those engaged in research, undergraduate and postgraduate students in higher education can also use this publication as a reference book. This book also presents a multifaceted perspective for the general public, including topics such as computer science, the food business, hotel management, fashion, medical, inventory management, and agricultural spheres. |
| data science and management impact factor: Artificial Intelligence and Data Science Ashwani Kumar, Iztok Fister Jr., P. K. Gupta, Johan Debayle, Zuopeng Justin Zhang, Mohammed Usman, 2022-12-13 This book constitutes selected papers presented at the First International Conference on Artificial Intelligence and Data Science, ICAIDS 2021, held in Hyderabad, India, in December 2021. The 43 papers presented in this volume were thoroughly reviewed and selected from the 195 submissions. They focus on topics of artificial intelligence for intelligent applications and data science for emerging technologies. |
| data science and management impact factor: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
