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Probabilistic Ranking Techniques in Relational Databases

ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data;

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Probabilistic Ranking Techniques in Relational Databases

ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data;

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Probabilistic Databases

-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query;

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Probabilistic Databases

-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query;

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Query Processing on Probabilistic Data

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database;

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SQL NoSQL Databases

This book offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the;

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Fundamentals of Object Databases

Object-oriented databases were originally developed as an alternative to relational database technology for the representation, storage;

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Inside Relational Databases

Marklyn and Whitehorn cover essential relational database theory and explain how to build databases securely - seeking to do this without;

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Inside Relational Databases

Marklyn and Whitehorn cover essential relational database theory and explain how to build databases securely - seeking to do this without;

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Introduction to Constraint Databases

generalization of relational databases. An important theme running through the text is showing how relational databases can smoothly develop into;

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Principles of Database Query Processing for Advanced Applications

. Traditional techniques work well for databases with standard, single-site relational structures, but databases containing more complex and diverse;

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Statistical Relational Artificial Intelligence

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Statistical Relational Artificial Intelligence

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Statistical Relational Artificial Intelligence

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Multilevel Security for Relational Databases

critically important. Introducing the concept of multilevel security in relational databases, this book provides a comparative study of the various;

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Multilevel Security for Relational Databases

critically important. Introducing the concept of multilevel security in relational databases, this book provides a comparative study of the various;

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Query Processing over Uncertain Databases

uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book;

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Query Processing over Uncertain Databases

uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book;

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Therapeutic Potential of Furin Inhibition

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Upgrading Relational Databases With Objects

Upgrading Relational Databases with Objects presents a clear-headed overview of how to use object-oriented (OO) technology to improve;

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Computational Learning And Probabilistic Reasoning

techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational;

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Machine Learning and Knowledge Discovery in Databases

and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches;

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Machine Learning and Knowledge Discovery in Databases

and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches;

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Machine Learning and Knowledge Discovery in Databases

; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical;

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Intelligent Big Multimedia Databases

relational model is not powerful enough to address multimedia data. Because of this, multimedia databases are categorized into many major areas;

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Managing Time in Relational Databases

Managing Time in Relational Databases: How to Design, Update and Query Temporal Data introduces basic concepts that will enable;

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NoSQL Data Models

. It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are;

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