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Query Processing on Probabilistic Data

community and the Al community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey;

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Probabilistic Databases

. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by;

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Probabilistic Databases

. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by;

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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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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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Advanced Query Processing

special focus on query processing and data integration. It is aimed for technologists, managers, and developers who want to know more about;

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Database Systems for Advanced Applications

summarization; security and privacy; outlier and imbalanced data analysis; probabilistic and uncertain data; query processing.;

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Principles of Database Query Processing for Advanced Applications

types of data demand new query processing and optimization techniques. Most real-world data is not well structured. Today's;

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Database Systems for Advanced Applications

submissions. The papers cover the following topics: big data management, indexing and query processing, graph data management, spatio-temporal data;

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Query Processing over Incomplete Databases

) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete;

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Database Systems for Advanced Applications

selected from a total of 159 submissions. The topics covered are query processing and optimization, data semantics, XML and semi-structured data;

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Database Systems for Advanced Applications

management; indexing and distributed systems; network embedding; trajectory and time series data processing; data mining; query processing and;

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Database Systems for Advanced Applications

; indexing and distributed systems; network embedding; trajectory and time series data processing; data mining; query processing and optimization;

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Beyond Traditional Probabilistic Data Processing Techniques

; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination;

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Web Information Systems and Applications

papers are grouped in topical sections on world wide web, recommendation, query processing and algorithm, natural language processing, machine;

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Big Data

range of topics such as query and update processing; relational storage; benchmarking; XML query processing; big data; spatial data and indexing;

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Datalog and Recursive Query Processing

In recent years, we have witnessed a revival of the use of recursive queries in a variety of emerging application domains such as data;

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Database Systems for Advanced Applications

; index; query analysis; XML data management; privacy protection; and uncertain data management; and in part 2: graph data management; physical;

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Database Systems for Advanced Applications

; index; query analysis; XML data management; privacy protection; and uncertain data management; and in part 2: graph data management; physical;

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Data Stream Management

on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types;

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Querying Graphs

: graph data models, graph query languages and graph query specification, graph constraints, and graph query processing. We conclude by indicating;

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Analyzing Time Interval Data

Philipp Meisen introduces a model, a query language, and a similarity measure enabling users to analyze time interval data. The introduced;

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Microsoft SQL Server 2008 T-SQL Fundamentals

code samples-that show you how to develop code to query and modify data. You'll gain a solid understanding of the T-SQL language and good;

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Probabilistic Data Structures for Blockchain-Based Internet of Things Applications

This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the;

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Database and Expert Systems Applications

Matching; Data Modeling, Data Abstraction, and Uncertainty; Preferences and Query Optimization; Data Integration and RDF Matching; Security and;

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