Products related to Big Data:
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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 behavior, 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 fields of automotive engineering, electrical engineering, and data mining.
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Big Data and Mobility as a Service
Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment.It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints.The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation.Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners.
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Tourism Innovation in the Digital Era : Big Data, AI and Technological Transformation
Digitalization and artificial intelligence are increasingly influencing modes of travel planning, exploration of new destinations, and promotion of them.The potential of new technologies to completely overhaul the tourism and hospitality industry is emerging; new generations of tourists will have radically different expectations and requirements in relation to today's tourists.The sharing economy and the experience economy strongly influence the creation of new business models in tourism.Many low-income economies can potentially benefit from this digital transformation and others are at risk of being left behind if they fail to embrace this moment. To explore how and why, ten chapters are presented here in Tourism Innovation in the Digital Era, ranging from topics as diverse as big data analytics in tourism marketing to the impact of AI to the use of CSR in competitiveness amongst hotel groups.Providing multidisciplinary approaches, readers will learn more about the organizational impacts of digitalization and artificial intelligence in tourism and hospitality. The book series New Perspectives in Tourism and Hospitality Management positions organizational change and behavior in the tourism and hospitality sector at its heart, providing interdisciplinary research in this field and joining the areas of organizational studies and tourism/hospitality management.
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Big Data for Urban Sustainability : A Human-Centered Perspective
This book presents a practical framework for the application of big data, cloud, and pervasive and complex systems to sustainable solutions for urban environmental challenges. It covers the technologies, potential, and possible and impact of big data on energy efficiency and the urban environment. The book first introduces key aspects of big data, cloud services, pervasive computing, and mobile technologies from a pragmatic design perspective, including sample open source firmware.Cloud services, mobile and embedded platforms, interfaces, operating system design methods, networking, and middleware are all considered.The authors then explore in detail the framework, design principles, architecture and key components of developing energy systems to support sustainable urban environments.The included case study provides a pathway to improve the eco-efficiency of urban transport, demonstrating how to design an energy efficient next generation urban navigation system by leveraging vast cloud data sets on user-behavior.Ultimately, this resource maps big data’s pivotal intersection with rapid global urbanization along the path to a sustainable future.
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What is Big Data?
Big Data refers to the large volume of structured and unstructured data that is generated by businesses and individuals on a daily basis. This data is too vast and complex to be processed using traditional data processing applications. Big Data is characterized by its volume, velocity, and variety, and it can provide valuable insights and trends when analyzed effectively. Organizations use Big Data analytics tools and technologies to extract meaningful information from this data to make informed decisions and improve their operations.
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What is the difference between Big Data and Smart Data?
Big Data refers to the vast amount of data that is generated from various sources, including social media, sensors, and other digital platforms. It is characterized by its volume, velocity, and variety. On the other hand, Smart Data refers to the meaningful and actionable insights that are derived from Big Data through advanced analytics and machine learning techniques. Smart Data focuses on extracting valuable information from the massive amount of data to make informed decisions and drive business outcomes. In essence, Big Data is the raw material, while Smart Data is the refined and processed information that can be used for strategic decision-making.
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What are the disadvantages of Big Data?
Some of the disadvantages of Big Data include privacy concerns, as the collection and analysis of large amounts of data can raise ethical and privacy issues. Another disadvantage is the potential for data overload, where organizations may struggle to effectively manage and analyze the vast amount of data they collect. Additionally, there can be challenges in ensuring the accuracy and quality of the data, as well as the potential for biases in the data collection and analysis processes. Finally, the cost of implementing and maintaining Big Data infrastructure and tools can be a significant disadvantage for some organizations.
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Can someone explain simply what Big Data is all about?
Big Data refers to the massive volume of structured and unstructured data that is generated by businesses and individuals on a daily basis. This data is too large and complex to be processed using traditional database management tools. Big Data technologies allow organizations to collect, store, and analyze this data to gain insights, make better decisions, and identify trends. In essence, Big Data is about harnessing the power of data to drive innovation, improve efficiency, and create value.
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Big Data for the Public Good : Regulating Access to Public Sector Big Data for Research and Innovation
Can researchers and innovators use UK public sector data to produce knowledge that improves policy making, scrutinises government work and promotes the public interest?This book looks at interactions between UK public sector officials and researchers/innovators to shed light on barriers to data access and use.It asks: what are the frameworks that govern access to public sector big datasets for researchers and innovators?How are these frameworks applied in practice? What are the governance solutions for policy makers interested in harnessing the untapped potential of public sector big data to improve their policies and create public benefit?Public sector data is a valuable resource that can help researchers and innovators create knowledge and solutions that benefit society.As public bodies collect increasingly more data about us, UK policy makers try to maximise the use of public sector big data for the benefit of the public.But accessing this data is not easy. There are many legal, technical, and ethical barriers that prevent the use of public sector data for research and innovation.This book is for researchers and innovators who want to understand and overcome the barriers to accessing UK public sector data.It is also for policy makers who are interested in how public sector data can be used to improve decision-making, scrutinise government work, and promote the public interest.
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Big Data
Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields.However, an epistemological study of these novel tools is still largely lacking.After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed.These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology.In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods.Based on this insight, the before-mentioned epistemological issues can be systematically addressed.
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Big Data Analytics
Big Data Analytics is intended for use as a textbook for third- and fourth-year students of B.E., B.Tech., B.Sc., BCA, MCA, and M.Tech. courses in IT, Software, and Computer Science Engineering.The book has been written to help students who enter the software industry to gain a broad understanding of Big Data and the nuances of handling it to extract useful information.Spread across 21 chapters, it elucidates the concept of Big Data and walks the reader through popular frameworks such as Hadoop, MongoDB, Pig and Hive that are used for processing Big Data.
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Big Data, Big Design : Why Designers Should Care about Artificial Intelligence
Big Data Big Design defines and explores what every designer needs to know about artificial intelligence (AI) and machine learning (ML), all the while inspiring designers to harness this technology and establish leadership via thoughtful, human-centered design.It’s not just about the algorithms, it’s about what we do with the data once received.Ellen lupton says, “ Important and accessible!” Readers will explore the principles and cultural context of Ai and ML, as well as gain an understanding of the design opportunities and pitfalls that arise as designers incorporate predictive algorithms into their practice.Designers will walk away from this portable, friendly book inspired by practical and theoretical knowledge that will allow them to make thoughtful decisions as this technology unfolds.
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How to calculate the amount of data in digital video data?
To calculate the amount of data in digital video data, you need to consider the resolution, frame rate, and bit depth of the video. First, calculate the total number of pixels in each frame by multiplying the width by the height of the video resolution. Then, multiply this by the number of frames per second to get the total number of pixels per second. Finally, multiply this by the bit depth (usually 8 bits per color channel) to get the total amount of data per second in bits.
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Do data get lost when updating from Catalina to Big Sur?
When updating from Catalina to Big Sur, it is possible for data to get lost, but it is not a common occurrence. However, it is always recommended to back up your important data before performing any major system updates. This way, you can ensure that your data is safe and easily recoverable in case anything goes wrong during the update process. It is also a good practice to check for compatibility issues with any third-party applications or hardware devices before updating to a new operating system.
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What does a timing diagram for preamble and payload data look like in digital technology?
In digital technology, a timing diagram for preamble and payload data typically shows the timing relationship between the preamble and the payload data. The preamble is a sequence of known bits that helps the receiver synchronize with the transmitter and establish a communication link. In the timing diagram, the preamble will be shown as a series of pulses or bits occurring at specific time intervals, followed by the payload data which contains the actual information being transmitted. The timing diagram will illustrate how the preamble and payload data are synchronized and transmitted within the digital communication system.
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How do you calculate the amount of data in digital video data?
The amount of data in digital video data can be calculated by multiplying the video resolution (width x height in pixels), the bit depth (number of bits used to represent each color channel), the frame rate (number of frames per second), and the duration of the video in seconds. This will give you the total amount of data in bits. To convert this to a more commonly used unit, such as megabytes or gigabytes, you can divide the total amount of data in bits by 8 to get the amount in bytes, and then divide by 1024 multiple times to convert to larger units.
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