Data Management and File Structures PDF Download

Author: Mary E. S. Loomis
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 564

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Author: Michael J. Folk
Publisher: Addison Wesley Publishing Company
ISBN: 9780201558715
Category : Computers
Languages : en
Pages : 212

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This book provides the conceptual tools to build file structures that can be quickly and efficiently accessed. It teaches good design judgment through an approach that puts the "hands-on" work of constructing and running programs at the center of the learning process. This best-selling book has been thoroughly updated. It includes timely coverage of file structures in a UNIX environment in addition to a new and substantial appendix on CD-ROM. All former programs in C and Pascal have been updated to ANSI C and Turbo Pascal 6.0. 0201557134B04062001

Author: Kristin Briney
Publisher: Pelagic Publishing Ltd
ISBN: 178427013X
Category : Computers
Languages : en
Pages : 312

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A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin

Author: Nils B. Weidmann
Publisher: Cambridge University Press
ISBN: 1108845673
Category : Social Science
Languages : en
Pages : 243

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Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data.

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309287812
Category : Mathematics
Languages : en
Pages : 191

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Book Description
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Author: Michael J. Folk
Publisher: Pearson Education India
ISBN: 9788177583731
Category :
Languages : en
Pages : 748

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Author: Rohit Khurana
Publisher: Vikas Publishing House
ISBN: 9325960052
Category : Computers
Languages : en
Pages : 403

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Book Description
Data and File Structure has been specifically designed to meet the requirements of the engineering students of GTU. This is a core subject in the curriculum of all Computer Science programs. The aim of this book is to help the students develop programming and algorithm analysis skills simultaneously such that they are able to design programs with maximum efficiency. C language has been used in the book to permit the execution of basic data structures in a variety of ways. Key Features 1. Simple and easy-to-follow text 2. Wide coverage of topics 3. Programming examples for clarity 4. Summary and exercises at the end of each chapter to test your knowledge 5. Answers to selected exercises 6. University question papers with answers 7. Objective type questions for practice

Author: Wilfried Lemahieu
Publisher: Cambridge University Press
ISBN: 1107186129
Category : Computers
Languages : en
Pages : 817

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Book Description
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.

Author: Lena Wiese
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110441411
Category : Computers
Languages : en
Pages : 374

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Book Description
Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions. This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market.

Author: United States. National Bureau of Standards
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 684

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