Advanced Query Processing

Intelligent Systems Reference Library

Book 36
Springer Science & Business Media
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This research book presents key developments, directions, and challenges concerning advanced query processing for both traditional and non-traditional data. A special emphasis is devoted to approximation and adaptivity issues as well as to the integration of heterogeneous data sources.

The book will prove useful as a reference book for senior undergraduate or graduate courses on advanced data management issues, which have a special focus on query processing and data integration. It is aimed for technologists, managers, and developers who want to know more about emerging trends in advanced query processing.

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About the author

Alberto Belussi is associate professor at the Computer Science Department of the University of Verona, Italy. His current research interests are: geographical information systems design, design of interfaces for update of geographical data, multi-resolution geographical databases and interoperability in geographical databases.

Barbara Catania is Associate Professor at the Department of Computer and Information Sciences of the University of Genova, Italy. She has been Visiting Researcher at the European Computer-Industry Research Center of Bull, ICL, and Siemens in Munich, Germany, and at the National University of Singapore. Her main research interests include: deductive and constraint databases, spatial and geographic databases, XML and Web databases, pattern management, indexing techniques, and access control.

Eliseo Clementini is associate professor of computer science at the Faculty of Engineering of the University of L'Aquila. His main research interests are about modelling and computational aspects of geographic information science.

Elena Ferrari is a full professor of Computer Science at the University of Insubria, Como (Italy). Her research activities are related to various aspects of data management systems, including web security, access control and privacy, multimedia databases, and temporal databases. She is a member of the ACM and senior member of IEEE.

Jain is director/founder of the Knowledge-Based Intelligent Engineering Systems Centre, located in the Division of Information Technology, Engineering and the Envvironment. He is a fellow of the Institution of Engineers, Australia.

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Additional Information

Publisher
Springer Science & Business Media
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Published on
Jul 28, 2012
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Pages
350
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ISBN
9783642283239
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Best For
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Language
English
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Genres
Computers / Databases / Data Mining
Computers / Intelligence (AI) & Semantics
Technology & Engineering / General
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Content Protection
This content is DRM protected.
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Cross disciplinary biometric systems help boost the performance of the conventional systems. Not only is the recognition accuracy significantly improved, but also the robustness of the systems is greatly enhanced in the challenging environments, such as varying illumination conditions. By leveraging the cross disciplinary technologies, face recognition systems, fingerprint recognition systems, iris recognition systems, as well as image search systems all benefit in terms of recognition performance. Take face recognition for an example, which is not only the most natural way human beings recognize the identity of each other, but also the least privacy-intrusive means because people show their face publicly every day. Face recognition systems display superb performance when they capitalize on the innovative ideas across color science, mathematics, and computer science (e.g., pattern recognition, machine learning, and image processing). The novel ideas lead to the development of new color models and effective color features in color science; innovative features from wavelets and statistics, and new kernel methods and novel kernel models in mathematics; new discriminant analysis frameworks, novel similarity measures, and new image analysis methods, such as fusing multiple image features from frequency domain, spatial domain, and color domain in computer science; as well as system design, new strategies for system integration, and different fusion strategies, such as the feature level fusion, decision level fusion, and new fusion strategies with novel similarity measures.
Foreword by Steven Pinker

Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.

By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.

Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?

Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences.

Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics.

This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

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