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Gran Área: Ciencias sociales
Área: Comunicación y medios
Subárea: Ciencias de la información (aspectos sociales)

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Resultado: 350 registro(s)

8322

Current Trends in Database Technology

EDBT 2006: EDBT 2006 Workshop PhD, DataX, IIDB, IIHA, ICSNW, QLQP, PIM, PaRMa, and Reactivity on the Web, Munich, Germany, March 26-31, 2006, Revised Selected Papers Vol. 4254

 

13th ed. Berlin, Heidelberg: Springer-Verlag GmbH, 2006.

Título de la serie/colección: Lecture Notes in Computer Science. ISSN 0302-9743,

ISBNs 9783540467885 9783540467908

Colección: SpringerLink


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23659

Cyber-Nationalism in China. Challenging Western media portrayals of internet censorship in China

 

University of Adelaide Press, 2012.

ISBNs 9780987171894

Colección: Directory of Open Access Books


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8368

Cyberspace Security and Defense

Research Issues: Proceedings of the NATO Advanced Research Workshop on Cyberspace Security and Defense: Research Issues Gdansk, Poland 6-9 September 2004 Vol. 196

 

13th ed. Dordrecht: Springer, 2005.

Título de la serie/colección: NATO Science Series II: Mathematics, Physics and Chemistry. ISSN 1568-2609,

ISBNs 9781402033797 9781402033810

Colección: SpringerLink


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23674

Data Information Literacy

Librarians, Data and the Education of a New Generation of Researchers

 

Purdue University Press, 2015.

Título de la serie/colección: Purdue Information Literacy Handbooks.

ISBNs 9781557536969 9781612493510

Colección: Directory of Open Access Books

Resumen (en inglés): Given the increasing attention to managing, publishing, and preserving research datasets as scholarly assets, what competencies in working with research data will graduate students in STEM disciplines need to be successful in their fields? And what role can librarians play in helping students attain these competencies? In addressing these questions, this book articulates a new area of opportunity for librarians and other information professionals, developing educational programs that introduce graduate students to the knowledge and skills needed to work with research data. The term “data information literacy” has been adopted with the deliberate intent of tying two emerging roles for librarians together. By viewing information literacy and data services as complementary rather than separate activities, the contributors seek to leverage the progress made and the lessons learned in each service area.


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17570

Data Mining

 

3rd ed. Morgan Kaufmann, 2011.

ISBNs 9780123748560

Colección: ScienceDirect

Resumen (en inglés):

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization


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17573

Data Mining

Concepts and Techniques

 

3rd ed. Morgan Kaufmann, 2012.

ISBNs 9780123814791

Colección: ScienceDirect

Resumen (en inglés): Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets.
After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

    * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data


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    17572

    Data Mining for Bioinformatics Applications

     

    Woodhead Publishing, 2015.

    ISBNs 9780081001004

    Colección: ScienceDirect

    Resumen (en inglés):

    Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation.

    The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation.

    • Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems
    • Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems
    • Contains 45 bioinformatics problems that have been investigated in recent research


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    17574

    Data Model Patterns

    A Metadata Map

     

    Morgan Kaufmann, 2006.

    ISBNs 9780120887989

    Colección: ScienceDirect

    Resumen (en inglés): In recent years, companies and government agencies have come to realize that the data they use represent a significant corporate resource, whose cost calls for management every bit as rigorous as the management of human resources, money, and capital equipment. With this realization has come recognition of the importance to integrate the data that has traditionally only been available from disparate sources.

    An important component of this integration is the management of the “metadata” that describe, catalogue, and provide access to the various forms of underlying business data. The “metadata repository” is essential keeping track both of the various physical components of these systems, but also their semantics. What do we mean by “customer?” Where can we find information about our customers?

    After years of building enterprise models for the oil, pharmaceutical, banking, and other industries, Dave Hay has here not only developed a conceptual model of such a metadata repository, he has in fact created a true enterprise data model of the information technology industry itself.

    * A comprehensive work based on the Zachman Framework for information architecture—encompassing the Business Owner's, Architect's, and Designer's views, for all columns (data, activities, locations, people, timing, and motivation)
    * Provides a step-by-step description of model and is organized so that different readers can benefit from different parts
    * Provides a view of the world being addressed by all the techniques, methods and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.)
    * Presents many concepts that are not currently being addressed by such tools — and should be


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    17575

    Data Preparation for Data Mining Using SAS

     

    Morgan Kaufmann, 2007.

    ISBNs 9780123735775

    Colección: ScienceDirect

    Resumen (en inglés): Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little “how to” information? And are you, like most analysts, preparing the data in SAS?

    This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.

    FEATURES
    * A complete framework for the data preparation process, including implementation details for each step.
    * The complete SAS implementation code, which is readily usable by professional analysts and data miners.
    * A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction.
    * Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.
    * CD includes dozens of SAS macros plus the sample data and the program for the book's case study.


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    17576

    Data Quality

    The Accuracy Dimension

     

    Morgan Kaufmann, 2003.

    ISBNs 9781558608917

    Colección: ScienceDirect

    Resumen (en inglés): Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.

    * Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes.

    * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy.

    * Is written by one of the original developers of data profiling technology.

    * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets.


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