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Books in Database management

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11-20 of 161 results in All results

Perspectives on Data Science for Software Engineering

  • 1st Edition
  • July 12, 2016
  • Tim Menzies + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 4 2 0 6 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 4 2 6 1 - 8
Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

Formative Assessment, Learning Data Analytics and Gamification

  • 1st Edition
  • May 9, 2016
  • Santi Caballé + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 6 3 7 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 6 6 7 - 9
Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance.

Big Data and Ethics

  • 1st Edition
  • April 27, 2016
  • Jérôme Béranger
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 0 2 5 - 6
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 1 0 6 2 - 4
Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of this “massive data” seems to be essential.This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature.

Relational Database Design and Implementation

  • 4th Edition
  • April 15, 2016
  • Jan L. Harrington
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 4 3 9 9 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 4 9 9 0 2 - 3
Relational Database Design and Implementation: Clearly Explained, Fourth Edition, provides the conceptual and practical information necessary to develop a database design and management scheme that ensures data accuracy and user satisfaction while optimizing performance. Database systems underlie the large majority of business information systems. Most of those in use today are based on the relational data model, a way of representing data and data relationships using only two-dimensional tables. This book covers relational database theory as well as providing a solid introduction to SQL, the international standard for the relational database data manipulation language. The book begins by reviewing basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL. Topics such as the relational data model, normalization, data entities, and Codd's Rules (and why they are important) are covered clearly and concisely. In addition, the book looks at the impact of big data on relational databases and the option of using NoSQL databases for that purpose.

Business Intelligence Strategy and Big Data Analytics

  • 1st Edition
  • April 7, 2016
  • Steve Williams
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 9 1 9 8 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 9 4 8 9 - 1
Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability.  It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion.  Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others.  It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges. In recent years, terms like “big data” and “big data analytics” have been introduced into the business and technical lexicon.  Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness.  Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate.  More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both.

Cloud Computing in Ocean and Atmospheric Sciences

  • 1st Edition
  • March 24, 2016
  • Tiffany C Vance + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 1 9 2 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 1 9 3 - 3
Cloud Computing in Ocean and Atmospheric Sciences provides the latest information on this relatively new platform for scientific computing, which has great possibilities and challenges, including pricing and deployments costs and applications that are often presented as primarily business oriented. In addition, scientific users may be very familiar with these types of models and applications, but relatively unfamiliar with the intricacies of the hardware platforms they use. The book provides a range of practical examples of cloud applications that are written to be accessible to practitioners, researchers, and students in affiliated fields. By providing general information on the use of the cloud for oceanographic and atmospheric computing, as well as examples of specific applications, this book encourages and educates potential users of the cloud. The chapters provide an introduction to the practical aspects of deploying in the cloud, also providing examples of workflows and techniques that can be reused in new projects.

The Visual Imperative

  • 1st Edition
  • March 9, 2016
  • Lindy Ryan
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 8 4 4 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 9 3 0 - 4
Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today’s most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. The world of data is changing fast. And, it’s becoming more visual. Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets.

Digital Libraries

  • 1st Edition
  • January 21, 2016
  • Fabrice Papy
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 0 4 5 - 4
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 0 4 8 6 - 9
The technological interoperability of digital libraries must be rethought in order to adapt to new uses and networks. Informative digital environments aimed at responding to heritage, cultural, scientific or commercial demands have taken over the global cyberspace and have redesigned the techno-informative landscape of the Web. However, while the technological models demonstrate their effectiveness and explain to a large extent the creation of digital libraries, archives and deposits, the subjacent concept of uses continues to cause debate. The information technologies used by heterogeneous digital libraries enable a technical interoperability of content. This is not enough to allow the adhesion of a public connected to very different information profiles and techniques. This book explores the avenues of a user-orientated interoperability where the questions of consultation interfaces and content description processes are studied.

Optimizing the Display and Interpretation of Data

  • 1st Edition
  • September 23, 2015
  • Robert Warner
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 4 5 1 3 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 5 3 4 1 - 6
"What information do these data reveal?" "Is the information correct?" "How can I make the best use of the information?" The widespread use of computers and our reliance on the data generated by them have made these questions increasingly common and important. Computerized data may be in either digital or analog form and may be relevant to a wide range of applications that include medical monitoring and diagnosis, scientific research, engineering, quality control, seismology, meteorology, political and economic analysis and business and personal financial applications. The sources of the data may be databases that have been developed for specific purposes or may be of more general interest and include those that are accessible on the Internet. In addition, the data may represent either single or multiple parameters. Examining data in its initial form is often very laborious and also makes it possible to "miss the forest for the trees" by failing to notice patterns in the data that are not readily apparent. To address these problems, this monograph describes several accurate and efficient methods for displaying, reviewing and analyzing digital and analog data. The methods may be used either singly or in various combinations to maximize the value of the data to those for whom it is relevant. None of the methods requires special devices and each can be used on common platforms such as personal computers, tablets and smart phones. Also, each of the methods can be easily employed utilizing widely available off-the-shelf software. Using the methods does not require special expertise in computer science or technology, graphical design or statistical analysis. The usefulness and accuracy of all the described methods of data display, review and interpretation have been confirmed in multiple carefully performed studies using independent, objective endpoints. These studies and their results are described in the monograph. Because of their ease of use, accuracy and efficiency, the methods for displaying, reviewing and analyzing data described in this monograph can be highly useful to all who must work with computerized information and make decisions based upon it.

Agile Data Warehousing for the Enterprise

  • 1st Edition
  • September 19, 2015
  • Ralph Hughes
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 3 9 6 4 6 4 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 3 9 6 5 1 8 - 9
Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs.  Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines.  Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.