Products related to Data:
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Fashion and Environmental Sustainability : Entrepreneurship, Innovation and Technology
The wide range of topics that the book covers are organised into sections reflecting a cradle to grave view of how entrepreneurial, innovative, and tech-savvy approaches can advance environmental sustainability in the fashion sector.These sections include: sustainable materials; innovation in design, range planning and product development; sustainable innovations in fashion supply chains; sustainable innovations in fashion retail and marketing; sustainable alternatives for end-of-life and circular economy initiatives; and more sustainable alternative fashion business models.
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Big Data and Electric Mobility
This book details how to assess electric mobility characteristics within electric vehicles, discussing energy management methods, automated systems, and the enormous potential of data resources mined from software, navigation systems and connectivity. Big Data and Electric Mobility presents methods to mine data specifically for electric vehicles, to comprehend their performance and to present opportunities to develop data-driven technological advancements.Including contributions from experts across the world, the book will look at topics such as human mobile behaviour, battery charging and health, powertrain simulation, energy management and multiphysics-constrained optimal charging. The book will be key reading for researchers and engineers in the field of automotive engineering, electrical engineering and data mining.
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Data Science Applied to Sustainability Analysis
Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis.As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions.In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas.
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The Enterprise Data Catalog : Improve Data Discovery, Ensure Data Governance, and Enable Innovation
Combing the web is simple, but how do you search for data at work?It's difficult and time-consuming, and can sometimes seem impossible.This book introduces a practical solution: the data catalog.Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog.You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog.Written from a data management perspective and from a library and information science perspective, this book helps you:Learn what a data catalog is and how it can help your organizationOrganize data and its sources into domains and describe them with metadataSearch data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphsManage the data in your company via a data catalogImplement a data catalog in a way that exactly matches the strategic priorities of your organizationUnderstand what the future has in store for data catalogs
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What is the difference between data integration and data consolidation?
Data integration involves combining data from different sources and formats into a single, unified view. This process ensures that the data is consistent and can be easily accessed and analyzed. On the other hand, data consolidation involves gathering data from different sources and storing it in a centralized location. This process helps to eliminate data silos and provides a single source of truth for the organization. In summary, data integration focuses on combining data for analysis and usage, while data consolidation focuses on centralizing data storage.
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What is the question about data protection and data security?
The question about data protection and data security revolves around how organizations can safeguard sensitive information from unauthorized access, breaches, and cyber threats. It also involves ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Additionally, the question may address best practices for data encryption, access controls, regular security audits, and incident response plans to mitigate risks and protect data integrity.
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What is the difference between data protection and data security?
Data protection refers to the policies and procedures put in place to ensure that personal data is handled in compliance with privacy laws and regulations. This includes obtaining consent for data collection, limiting access to personal information, and ensuring data is accurate and up to date. On the other hand, data security focuses on protecting data from unauthorized access, use, or destruction. This includes measures such as encryption, firewalls, and access controls to prevent data breaches and cyber attacks. In summary, data protection is about the legal and ethical handling of personal data, while data security is about the technical measures used to protect that data from unauthorized access or misuse.
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What are examples of data security?
Examples of data security measures include encryption, which involves converting data into a code that can only be accessed with the correct key; access controls, which limit who can view or modify data; firewalls, which protect networks from unauthorized access; and regular data backups to ensure that information can be restored in the event of a security breach or data loss. Additionally, strong password policies, multi-factor authentication, and security training for employees are also important components of data security.
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Innovation in Energy Security and Long-Term Energy Efficiency
The sustainable development of our planet depends on the use of energy. The growing population of the world inevitably causes an increase in the demand for energy, which, on the one hand, threatens the potential for shortages of energy supply, and, on the other hand, causes the deterioration of the environment.Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play a role in affecting the behavior of households and companies, by causing behavioral change oriented to energy stewardship, and an overall switch to renewable energy resources. This book provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate the driving of societies to long-term energy efficiency.
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Handbook of Mobility Data Mining, Volume 1 : Data Preprocessing and Visualization
Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach.The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems.The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. Further, the book introduces how to design MDM platforms that adapt to the evolving mobility environment, new types of transportation, and users based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field.This volume focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow.The book first provides a conceptual theory and framework, then discusses data sources, trajectory map-matching, noise filtering, trajectory data segmentation, data quality assessment, and more, concluding with a chapter on privacy protection in mobile big data mining.
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Dynamic Efficiency and Productivity Measurement
A systematic treatment of dynamic decision making and performance measurementModern business environments are dynamic.Yet, the models used to make decisions and quantify success within them are stuck in the past.In a world where demands, resources, and technology are interconnected and evolving, measures of efficiency need to reflect that environment. In Dynamic Efficiency and Productivity Measurement, Elvira Silva, Spiro E.Stefanou, and Alfons Oude Lansink look at the business process from a dynamic perspective.Their systematic study covers dynamic production environments where current production decisions impact future production possibilities.By considering practical factors like adjustments over time, this book offers an important lens for contemporary microeconomic analysis.Silva, Stefanou, and Lansink develop the analytical foundations of dynamic production technology in both primal and dual representations, with an emphasis on directional distance functions.They cover concepts measuring the production structure (economies of scale, economies of scope, capacity utilization) and performance (allocative, scale and technical inefficiency, productivity) in a methodological and comprehensive way. Through a unified approach, Dynamic Efficiency and Productivity Measurement offers a guide to how firms maximize potential in changing environments and an invaluable contribution to applied microeconomics.
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Productivity Machines : German Appropriations of American Technology from Mass Production to Computer Automation
How productivity culture and technology became emblematic of the American economic system in pre- and postwar Germany. The concept of productivity originated in a statistical measure of output per worker or per work-hour, calculated by the US Bureau of Labor Statistics.A broader productivity culture emerged in 1920s America, as Henry Ford and others linked methods of mass production and consumption to high wages and low prices.These ideas were studied eagerly by a Germany in search of economic recovery after World War I, and, decades later, the Marshall Plan promoted productivity in its efforts to help post-World War II Europe rebuild.In Productivity Machines, Corinna Schlombs examines the transatlantic history of productivity technology and culture in the two decades before and after World War II.She argues for the interpretive flexibility of productivity: different groups viewed productivity differently at different times.Although it began as an objective measure, productivity came to be emblematic of the American economic system; post-World War II West Germany, however, adapted these ideas to its own political and economic values.Schlombs explains that West German unionists cast a doubtful eye on productivity's embrace of plant-level collective bargaining; unions fought for codetermination-the right to participate in corporate decisions.After describing German responses to US productivity, Schlombs offers an in-depth look at labor relations in one American company in Germany-that icon of corporate America, IBM.Finally, Schlombs considers the emergence of computer technology-seen by some as a new symbol of productivity but by others as the means to automate workers out of their jobs.
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What is data protection and security?
Data protection and security refer to the measures and practices put in place to safeguard data from unauthorized access, use, or disclosure. This includes protecting data from cyber threats such as hacking, malware, and phishing attacks, as well as ensuring compliance with privacy regulations and laws. Data protection and security also involve implementing encryption, access controls, and regular data backups to prevent data loss and maintain the integrity of sensitive information. Overall, data protection and security are essential for maintaining the confidentiality, integrity, and availability of data.
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Can economic efficiency and productivity develop mutually?
Yes, economic efficiency and productivity can develop mutually. When businesses and industries become more efficient in their operations, they can produce more output with the same amount of input, leading to increased productivity. Similarly, when productivity increases, it can drive economic efficiency by reducing waste and improving resource allocation. Therefore, as businesses and industries focus on improving efficiency and productivity, they can reinforce and support each other's development.
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What is the difference between efficiency and productivity?
Efficiency refers to how well resources are utilized to achieve a specific goal or output, while productivity measures the output or results generated from a specific amount of input or resources. Efficiency focuses on minimizing waste and maximizing output with the resources available, while productivity is a measure of how much output is produced relative to the input used. In essence, efficiency is about doing things right, while productivity is about doing the right things.
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What are the connections between efficiency and productivity?
Efficiency and productivity are closely connected in that efficiency refers to the ability to accomplish a task with minimal waste, effort, or cost, while productivity refers to the rate at which goods or services are produced. When a process or system is efficient, it can lead to increased productivity because it allows for more output to be generated with the same amount of input. Conversely, when productivity is high, it often indicates that the resources and processes are being used efficiently. Therefore, improving efficiency can lead to increased productivity, and vice versa, as they both contribute to the overall effectiveness of a business or organization.
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