[PDF] Big Data Big Design eBook

Author : Helen Armstrong
Publisher : Chronicle Books
Page : 177 pages
File Size : 39,67 MB
Release : 2021-11-04
Category : Design
ISBN : 1648960782

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Big Data, Big Design provides designers with the tools they need to harness the potential of machine learning and put it to use for good through thoughtful, human-centered, intentional design. Enter the world of Machine Learning (ML) and Artificial Intelligence (AI) through a design lens in this thoughtful handbook of practical skills, technical knowledge, interviews, essays, and theory, written specifically for designers. Gain an understanding of the design opportunities and design biases that arise when using predictive algorithms. Learn how to place design principles and cultural context at the heart of AI and ML through real-life case studies and examples. This portable, accessible guide will give beginners and more advanced AI and ML users the confidence to make reasoned, thoughtful decisions when implementing ML design solutions.

Author : Viktor Mayer-Schönberger
Publisher : Houghton Mifflin Harcourt
Page : 257 pages
File Size : 24,98 MB
Release : 2013
Category : Business & Economics
ISBN : 0544002695

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A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Author : David E. Carter
Publisher : Collins Design
Page : 492 pages
File Size : 26,69 MB
Release : 2000
Category : Design
ISBN : 9780688179861

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This major new reference contains an assemblage of visual concepts from around the world. Categories include designs for annual reports, books, calenders, catalogs, editorial layouts, exhibits, labels and tags, letterheads, menus, outdoor advertising, packaging, posters, promotion materials, shopping bags, T-shirts, and more. 900 color illustrations.

Author : Martin Kleppmann
Publisher : "O'Reilly Media, Inc."
Page : 658 pages
File Size : 30,68 MB
Release : 2017-03-16
Category : Computers
ISBN : 1491903104

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Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

Author : Dhabaleswar K. Panda
Publisher : MIT Press
Page : 275 pages
File Size : 31,58 MB
Release : 2022-08-02
Category : Computers
ISBN : 0262369427

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An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

Author : Helen Armstrong
Publisher : Chronicle Books
Page : 156 pages
File Size : 16,6 MB
Release : 2016-06-28
Category : Design
ISBN : 1616894954

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Digital Design Theory bridges the gap between the discourse of print design and interactive experience by examining the impact of computation on the field of design. As graphic design moves from the creation of closed, static objects to the development of open, interactive frameworks, designers seek to understand their own rapidly shifting profession. Helen Armstrong's carefully curated introduction to groundbreaking primary texts, from the 1960s to the present, provides the background necessary for an understanding of digital design vocabulary and thought. Accessible essays from designers and programmers are by influential figures such as Ladislav Sutnar, Bruno Munari, Wim Crouwel, Sol LeWitt, Muriel Cooper, Zuzana Licko, Rudy VanderLans, John Maeda, Paola Antonelli, Luna Maurer, and Keetra Dean Dixon. Their topics range from graphic design's fascination with programmatic design, to early strivings for an authentic digital aesthetic, to the move from object-based design and to experience-based design. Accompanying commentary assesses the relevance of each excerpt to the working and intellectual life of designers.

Author : Steve Wexler
Publisher : McGraw Hill Professional
Page : 208 pages
File Size : 43,66 MB
Release : 2021-05-18
Category : Business & Economics
ISBN : 1260473538

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Not a data expert? Here’s an engaging and entertaining guide to interpreting and drawing insights from any chart, graph, or other data visualization you’ll encounter. You’re a business professional, not a data scientist. How do you make heads or tails of the data visualizations that come across your desk—let alone make critical business decisions based on the information they’re designed to convey? In The Big Picture, top data visualization consultant Steve Wexler provides the tools for developing the graphical literacy you need to understand the data visualizations that are flooding your inbox—and put that data to use. Packed with the best four-color examples created in Excel, Tableau, Power BI, and Qlik, among others, this one-stop resource empowers you to extract the most important information from data visualizations quickly and accurately, act on key insights, solve problems, and make the right decisions for your organization every time.

Author : James Warren
Publisher : Simon and Schuster
Page : 481 pages
File Size : 35,11 MB
Release : 2015-04-29
Category : Computers
ISBN : 1638351104

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Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

Author : Giorgia Lupi
Publisher : Chronicle Books
Page : 304 pages
File Size : 40,12 MB
Release : 2016-09-13
Category : Design
ISBN : 1616895462

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Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.

Author : Jeremy Kepner
Publisher : MIT Press
Page : 443 pages
File Size : 14,60 MB
Release : 2018-08-07
Category : Computers
ISBN : 0262347911

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The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.