Products related to Big Data:
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Big Data and Blockchain Technology for Secure IoT Applications
Big Data and Blockchain Technology for Secure IoT Applications presents a comprehensive exploration of the intersection between two transformative technologies: big data and blockchain, and their integration into securing Internet of Things (IoT) applications.As the IoT landscape continues to expand rapidly, the need for robust security measures becomes paramount to safeguard sensitive data and ensure the integrity of connected devices.This book delves into the synergistic potential of leveraging big data analytics and blockchain’s decentralized ledger system to fortify IoT ecosystems against various cyber threats, ranging from data breaches to unauthorized access.Within this groundbreaking text, readers will uncover the foundational principles underpinning big data analytics and blockchain technology, along with their respective roles in enhancing IoT security.Through insightful case studies and practical examples, this book illustrates how organizations across diverse industries can harness the power of these technologies to mitigate risks and bolster trust in IoT deployments.From real-time monitoring and anomaly detection to immutable data storage and tamper-proof transactions, the integration of big data and blockchain offers a robust framework for establishing secure, transparent, and scalable IoT infrastructures.Furthermore, this book serves as a valuable resource for researchers, practitioners, and policymakers seeking to navigate the complexities of IoT security.By bridging the gap between theory and application, this book equips readers with the knowledge and tools necessary to navigate the evolving landscape of interconnected devices while safeguarding against emerging cyber threats.With contributions from leading experts in the field, it offers a forward-thinking perspective on harnessing the transformative potential of big data and blockchain to realize the full promise of the IoT securely.
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Stochastic Modelling of Big Data in Finance
Stochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of stochastic modelling of big data in finance (BDF).The book describes various stochastic models, including multivariate models, to deal with big data in finance.This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set.The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance. FeaturesSelf-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big dataAll results are presented visually to aid in understanding of conceptsDr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine, and is a recipient of NASU award for young scientist with a gold medal for series of research publications in random evolutions and their applications. Dr. Swishchuk is a chair and organizer of finance and energy finance seminar ‘Lunch at the Lab’ at the Department of Mathematics and Statistics.Dr. Swishchuk is a Director of Mathematical and Computational Finance Laboratory at the University of Calgary.He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steering committee member of Global Association of Risk Professionals (GARP), Canada (since 2015). Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Statistics.He is also a proponent for a new specialization “Financial and Energy Markets Data Modelling” in the Data Science and Analytics program.His research areas include financial mathematics, random evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals.He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals.In 2018 he received a Peak Scholar award.
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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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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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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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How to finance a big trip?
There are several ways to finance a big trip. One option is to save up money over time by cutting back on expenses and setting aside a portion of your income specifically for the trip. Another option is to look for additional sources of income, such as taking on a part-time job or freelance work. You could also consider selling items you no longer need or using a travel rewards credit card to earn points or miles that can be used towards your trip. Lastly, you could explore crowdfunding platforms or seek out sponsorships from companies or organizations that align with your travel plans.
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Big Data
The internet has launched the world into an era into which enormous amounts of data aregenerated every day through technologies with both positive and negative consequences. This often refers to big data . This book explores big data in organisations operating in thecriminology and criminal justice fields. Big data entails a major disruption in the ways we think about and do things, whichcertainly applies to most organisations including those operating in the criminology andcriminal justice fields.Big data is currently disrupting processes in most organisations – howdifferent organisations collaborate with one another, how organisations develop productsor services, how organisations can identify, recruit, and evaluate talent, how organisationscan make better decisions based on empirical evidence rather than intuition, and howorganisations can quickly implement any transformation plan, to name a few. All these processes are important to tap into, but two underlying processes are criticalto establish a foundation that will permit organisations to flourish and thrive in the era ofbig data – creating a culture more receptive to big data and implementing a systematic dataanalytics-driven process within the organisation. Written in a clear and direct style, this book will appeal to students and scholars incriminology, criminal justice, sociology, and cultural studies but also to governmentagencies, corporate and non-corporate organisations, or virtually any other institutionimpacted by big data.
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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 for Big Decisions : Building a Data-Driven Organization
Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term.Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval.This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs.The emphasis is on the big decisions – the key decisions that influence 90% of business outcomes – starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive.While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics.They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven.As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven.Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects.Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago.The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics.It is a practitioners’ handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization.
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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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How do you finance a big trip?
Financing a big trip can be done through a variety of methods. One common approach is to save up money over time by cutting back on expenses, setting a budget, and putting aside a portion of your income specifically for the trip. Another option is to look for additional sources of income, such as taking on a part-time job or freelance work. Some people also choose to use travel rewards credit cards or crowdfunding platforms to help fund their travels. Ultimately, careful planning and discipline are key to successfully financing a big trip.
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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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