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Elsevier

Morgan Kaufmann

  • Conceptual Design for Interactive Systems

    Designing for Performance and User Experience
    • 1st Edition
    • Avi Parush
    • English
    Conceptual Design for Interactive Systems: Designing for Performance and User Experience provides readers with a comprehensive guide to the steps necessary to take the leap from research and requirements to product design. The text presents a proven strategy for transforming research into a conceptual model, discussing the iterative process that allows users to build the essential foundation for a successful interactive system, while also taking the users’ mental model into consideration. Readers will gain a better understanding of the framework they need to perceive, understand, and experience their tasks and processes in the context of their products. The text is ideal for those seeking a proven, innovative strategy for meeting goals through intuitive and effective thinking.

  • Improving the User Experience through Practical Data Analytics

    Gain Meaningful Insight and Increase Your Bottom Line
    • 1st Edition
    • Mike Fritz + 1 more
    • English
    Improving the User Experience through Practical Data Analytics shows you how to make UX design decisions based on data—not hunches. Authors Fritz and Berger help the UX professional recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual inquiries. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. By mastering the use of these techniques, you’ll delight your users, increase your bottom line and gain a powerful competitive advantage for your company—and yourself. Key features include: Practical advise on choosing the right data analysis technique for each project. A step-by-step methodology for applying each technique, including examples and scenarios drawn from the UX field. Detailed screen shots and instructions for performing the techniques using Excel (both for PC and Mac) and SPSS. Clear and concise guidance on interpreting the data output. Exercises to practice the techniques

  • Green Information Technology

    A Sustainable Approach
    • 1st Edition
    • Mohammad Dastbaz + 2 more
    • English
    We are living in the era of "Big Data" and the computing power required to deal with "Big Data" both in terms of its energy consumption and technical complexity is one of the key areas of research and development. The U.S. Environmental Protection Agency estimates that centralized computing infrastructures (data centres) currently use 7 giga watts of electricity during peak loads. This translates into about 61 billion kilowatt hours of electricity used. By the EPA’s estimates, power-hungry data centres consume the annual output of 15 average-sized power plants. One of the top constraints to increasing computing power, besides the ability to cool, is simply delivering enough power to a given physical space. Green Information Technology: A Sustainable Approach offers in a single volume a broad collection of practical techniques and methodologies for designing, building and implementing a green technology strategy in any large enterprise environment, which up until now has been scattered in difficult-to-find scholarly resources. Included here is the latest information on emerging technologies and their environmental impact, how to effectively measure sustainability, discussions on sustainable hardware and software design, as well as how to use big data and cloud computing to drive efficiencies and establish a framework for sustainability in the information technology infrastructure. Written by recognized experts in both academia and industry, Green Information Technology: A Sustainable Approach is a must-have guide for researchers, computer architects, computer engineers and IT professionals with an interest in greater efficiency with less environmental impact.

  • Cloud Data Centers and Cost Modeling

    A Complete Guide To Planning, Designing and Building a Cloud Data Center
    • 1st Edition
    • Caesar Wu + 1 more
    • English
    Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundation of cloud concepts and definitions, the book addresses data center creation, infrastructure development, cost modeling, and simulations in decision-making, each part building on the previous. In this way the authors bridge technology, management, and infrastructure as a service, in one complete guide to data centers that facilitates educated decision making.

  • Systems Programming

    Designing and Developing Distributed Applications
    • 1st Edition
    • Richard Anthony
    • English
    Systems Programming: Designing and Developing Distributed Applications explains how the development of distributed applications depends on a foundational understanding of the relationship among operating systems, networking, distributed systems, and programming. Uniquely organized around four viewpoints (process, communication, resource, and architecture), the fundamental and essential characteristics of distributed systems are explored in ways which cut across the various traditional subject area boundaries. The structures, configurations and behaviours of distributed systems are all examined, allowing readers to explore concepts from different perspectives, and to understand systems in depth, both from the component level and holistically.

  • Communicating the UX Vision

    13 Anti-Patterns That Block Good Ideas
    • 1st Edition
    • Martina Schell + 1 more
    • English
    This book identifies the 13 main challenges designers face when they talk about their work and provides communication strategies so that a better design, not a louder argument, is what makes it into the world. It is a fact that we all want to put great design into the world, but no product ever makes it out of the building without rounds of reviews, feedback, and signoff. As an interaction or UX designer, you’ve felt the general trend toward faster development, more work, and less discussion. As we spend time crafting, we become attached to our own ideas and it gets all too easy to react to feedback emotionally or dismiss it, when we should be taking the time to decode it and explain or adapt the design. Communicating the UX Vision helps you identify the skills and behavioral patterns to present your work in more persuasive ways, and respond more constructively to feedback from coworkers and stakeholders.

  • Bio-Inspired Computation in Telecommunications

    • 1st Edition
    • Xin-She Yang + 2 more
    • English
    Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

  • Sharing Data and Models in Software Engineering

    • 1st Edition
    • Tim Menzies + 4 more
    • English
    Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects.

  • Reliability Assurance of Big Data in the Cloud

    Cost-Effective Replication-Based Storage
    • 1st Edition
    • Yun Yang + 2 more
    • English
    With the rapid growth of Cloud computing, the size of Cloud data is expanding at a dramatic speed. A huge amount of data is generated and processed by Cloud applications, putting a higher demand on cloud storage. While data reliability should already be a requirement, data in the Cloud needs to be stored in a highly cost-effective manner. This book focuses on the trade-off between data storage cost and data reliability assurance for big data in the Cloud. Throughout the whole Cloud data lifecycle, four major features are presented: first, a novel generic data reliability model for describing data reliability in the Cloud; second, a minimum replication calculation approach for meeting a given data reliability requirement to facilitate data creation; third, a novel cost-effective data reliability assurance mechanism for big data maintenance, which could dramatically reduce the storage space needed in the Cloud; fourth, a cost-effective strategy for facilitating data creation and recovery, which could significantly reduce the energy consumption during data transfer.

  • Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

    • 1st Edition
    • Leonidas Deligiannidis + 1 more
    • English
    Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors’ Leonidas Deligiannidis and Hamid Arabnia cover; Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. How to use image processing and visualization to analyze big data.

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