Products related to Big Data:
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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, Data Mining and Data Science : Algorithms, Infrastructures, Management and Security
Through the application of cutting-edge techniques like Big Data, Data Mining, and Data Science, it is possible to extract insights from massive datasets.These methodologies are crucial in enabling informed decision-making and driving transformative advancements across many fields, industries, and domains.This book offers an overview of latest tools, methods and approaches while also highlighting their practical use through various applications and case studies.
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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 Earth Data in Support of the Sustainable Development Goals (2022) - China
This open access book showcases the innovative practices of Big Earth Data methods through a collection of comprehensive case studies from China to monitor and evaluate indicators for seven SDGs, i.e., zero hunger (SDG 2), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), climate action (SDG 13), life below water (SDG 14), life on land (SDG 15), and to analyze the interactions among multiple SDGs indicators. The emphasis on Big Earth Data is highly relevant within the context of growing global challenges.Disaster risk mitigation, climate change, global food security, resource management, and environmental challenges all are interlinked through earth systems and processes that are independent of human constructs.Therefore, these case studies highlight methods and practices of spatial information mining and integrated SDG evaluation, which include evaluating the synergy and trade-off relationships among the SDGs in the context of their correlations; simulating multiple indicators’ interactions in future environmental, economic and social scenarios in the context of their temporal variations; designing integrated evaluations of regional SDGs in the context of experience with the study of multiple indicators.Big Earth Data therefore has the potential to support informed policy and decision support at global, regional, and local scales.
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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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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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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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Algorithms : Big Data, Optimization Techniques, Cyber Security
Algorithms are ubiquitous in the contemporary technological world, and they ultimately consist of finite sequences of instructions used to accomplish tasks with necessary input values.This book analyses the top performing algorithms in areas as diverse as Big Data, Artificial Intelligence, Optimization Techniques and Cloud & Cyber Security Systems in order to explore their power and limitations.
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Google Cloud Platform for Data Science : A Crash Course on Big Data, Machine Learning, and Data Analytics Services
This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications.GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics.Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI.The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will LearnSet up a GCP account and projectExplore BigQuery and its use cases, including machine learningUnderstand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning modelsExplore Google Cloud Dataproc and its use cases for big data processingCreate and share data visualizations and reports with Looker Data StudioExplore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud DataflowExplore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streamingWho This Book Is ForData scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects
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The Enterprise Big Data Lake : Delivering the Promise of Big Data and Data Science
The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities.But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises.You'll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book.Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise.Then, in a collection of essays about data lake implementation, you'll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries.Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries
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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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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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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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