Products related to Data:
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Digital Natives: Blockchain, NFT, Cryptocurrency
In the first book of our Digital Native collection, navigate the fascinating world of emerging technologies like blockchain, cryptocurrencies and NFTs!In this introductory book, we break down complex terms through beautiful illustrations in a virtual world called Metaverse.
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Data Mining for Social Network Data
Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science.This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis.It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well.It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.
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Blockchain and Cryptocurrency
Blockchain is a system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system and this is done using Cryptography.A Cryptocurrency is a digital money that operates on the Blockchain technology.This book is intended to provide a detailed analysis of the effect of Blockchain and Cryptocurrency in the current market.It is designed with the focus to facilitate the students in understanding the working behind the raging technology.Blockchain and Cryptocurrency are vastly discussed nowadays in all research domains to bring decentralization.This book gives easy access to the complex terminologies revolving around the technology.Since this is a new course assigned in the curriculum, therefore special care is taken in drafting the units.The chapters have been written with reference to the past understanding of the students of cryptography and merging those concepts with the Blockchain technology. The emphasis is laid on fundamental ideas and ease of understanding with suitable diagrammatic representation and real world examples.This book is self-contained and requires no comparison and analysis with other conceptual data.It can also be used by scholars who are beginners in the Blockchain technology and wish to dive deeper in its technological aspects.Numerous multiple choice questions at the back of each chapter are given to test the objective understanding of the concepts.Salient Features:Standard and precise definitions and tips. Popular blockchain consensus protocols, their strengths, weaknesses and application scenarios through analysis and comparison. Several blockchain examples essential for engineering students. Hints and explanations are given in the solution of every problem. Imperative real examples
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Data Mining : The Textbook
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks.Until now, no single book has addressed all these topics in a comprehensive and integrated way.The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis.These chapters comprehensively discuss a wide variety of methods for these problems.Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation.The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition.It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background).Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology"This is the most amazing and comprehensive text book on data mining.It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S.Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Price: 44.99 £ | Shipping*: 0.00 £
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Do you know a good cryptocurrency wallet?
Yes, one popular and highly recommended cryptocurrency wallet is the Ledger Nano S. It is a hardware wallet that provides secure storage for various cryptocurrencies and offers features like two-factor authentication and backup and recovery options. Another good option is the Trezor Model T, which also offers secure storage and easy-to-use interface for managing multiple cryptocurrencies. Both wallets are known for their security features and user-friendly design, making them popular choices among cryptocurrency users.
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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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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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Is digital data protection important to you?
Yes, digital data protection is very important to me. As a consumer, I want to ensure that my personal information is secure and not vulnerable to cyber attacks or data breaches. As a professional, I understand the importance of protecting sensitive business data and maintaining the trust of clients and customers. In today's digital age, data protection is essential for both personal and professional security.
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Principles of Data Mining
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas.It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples.The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress.A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.
Price: 39.99 £ | Shipping*: 0.00 £ -
Data Mining : The Textbook
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks.Until now, no single book has addressed all these topics in a comprehensive and integrated way.The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis.These chapters comprehensively discuss a wide variety of methods for these problems.Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation.The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition.It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background).Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology"This is the most amazing and comprehensive text book on data mining.It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S.Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Price: 62.99 £ | Shipping*: 0.00 £ -
Cryptocurrency Compliance and Operations : Digital Assets, Blockchain and DeFi
Cryptocurrencies and digital assets are increasingly garnering interest from institutional investors.This is on top of the already strong support in place for cryptocurrencies such as Bitcoin from the retail investor.With this rapid growth has come a series of complex operational and regulatory compliance challenges.These challenges have become further exacerbated by the increasing pace of technological advances in areas such as decentralized finance (DeFi) tokenization, blockchain and distributed ledger technology (DLT) essential to the crypto and digital asset markets.This book will be the first book to provide current and practical guidance on the operational and compliance foundations of crypto investing and asset management. The book will include:· Step-by-step analysis of the modern operational mechanics behind cryptocurrency investment operations· Detailed guidance and example documentation on the procedures launching a crypto fund· Explanation of the operational procedures and compliance requirements for crytpo asset managers· Detailed analysis of crypto anti-money laundering compliance, regulations and laws for cryptocurrencies· Up-to-date analysis of recent crypto case studies, frauds and regulatory enforcement actions· Review of the digital asset landscape including non-fungible tokens (NFTs) and asset tokenization· Current examples of real-world crypto operations policies and compliance manuals· Analysis of the emerging trends in crypto operations and compliance in areas including blockchain, DeFi,crypto lending, yield farming, crypto mining and dAppsCryptocurrency Compliance and Operations will be an invaluable up-to-date resource for investors, fund managers, and their operations and compliance personnel as well as service providers on the implementation and management of best practice operations.?
Price: 74.99 £ | Shipping*: 0.00 £ -
Cryptocurrency Compliance and Operations : Digital Assets, Blockchain and DeFi
Cryptocurrencies and digital assets are increasingly garnering interest from institutional investors.This is on top of the already strong support in place for cryptocurrencies such as Bitcoin from the retail investor.With this rapid growth has come a series of complex operational and regulatory compliance challenges.These challenges have become further exacerbated by the increasing pace of technological advances in areas such as decentralized finance (DeFi) tokenization, blockchain and distributed ledger technology (DLT) essential to the crypto and digital asset markets.This book will be the first book to provide current and practical guidance on the operational and compliance foundations of crypto investing and asset management. The book will include:· Step-by-step analysis of the modern operational mechanics behind cryptocurrency investment operations· Detailed guidance and example documentation on the procedures launching a crypto fund· Explanation of the operational procedures and compliance requirements for crytpo asset managers· Detailed analysis of crypto anti-money laundering compliance, regulations and laws for cryptocurrencies· Up-to-date analysis of recent crypto case studies, frauds and regulatory enforcement actions· Review of the digital asset landscape including non-fungible tokens (NFTs) and asset tokenization· Current examples of real-world crypto operations policies and compliance manuals· Analysis of the emerging trends in crypto operations and compliance in areas including blockchain, DeFi,crypto lending, yield farming, crypto mining and dAppsCryptocurrency Compliance and Operations will be an invaluable up-to-date resource for investors, fund managers, and their operations and compliance personnel as well as service providers on the implementation and management of best practice operations.?
Price: 49.99 £ | Shipping*: 0.00 £
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Can I own a cryptocurrency wallet as a minor?
Yes, minors can own a cryptocurrency wallet. However, the legal implications of owning and using cryptocurrency as a minor can vary depending on the jurisdiction. Some countries may have specific regulations regarding minors and cryptocurrency ownership, so it's important to research and understand the laws in your area before setting up a wallet. Additionally, minors may need to have a parent or guardian involved in the process of setting up and managing the wallet.
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Does Bitcoin mining on an Android mobile device consume data volume?
Yes, Bitcoin mining on an Android mobile device does consume data volume. Mining involves solving complex mathematical problems to validate transactions on the blockchain, which requires a significant amount of data to be transmitted and received. This data consumption can vary depending on the mining software used, the mining pool, and the frequency of mining activity. It is important for users to be aware of their data usage limits and potential charges from their mobile network provider when mining Bitcoin on their Android device.
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Is the Apriori algorithm associated with machine learning or data mining?
The Apriori algorithm is associated with data mining. It is a classic algorithm used for association rule mining in large datasets. It is specifically used to identify frequent itemsets in transactional databases. While it is often used in the context of machine learning tasks, its primary application is in data mining for discovering patterns and relationships in data.
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How does data transmission from analog to digital work?
Data transmission from analog to digital works by first converting the analog signal into a digital format. This is done through a process called sampling, where the analog signal is measured at regular intervals and each measurement is assigned a digital value. The digital data is then encoded and modulated for transmission over a digital communication channel, such as a network or the internet. At the receiving end, the digital signal is demodulated and decoded back into its original analog form for use. This process allows for more efficient and reliable transmission of data over long distances.
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