Probabilistic Databases

Probabilistic Databases

Author: Dan Suciu

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 164

ISBN-13: 3031018796

DOWNLOAD EBOOK

Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques


Book Synopsis Probabilistic Databases by : Dan Suciu

Download or read book Probabilistic Databases written by Dan Suciu and published by Springer Nature. This book was released on 2022-05-31 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques

Probabilistic Databases

Probabilistic Databases

Author: Dan Suciu

Publisher: Morgan & Claypool Publishers

Published: 2011

Total Pages: 183

ISBN-13: 1608456803

DOWNLOAD EBOOK

Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques


Book Synopsis Probabilistic Databases by : Dan Suciu

Download or read book Probabilistic Databases written by Dan Suciu and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques

Advances in Probabilistic Databases for Uncertain Information Management

Advances in Probabilistic Databases for Uncertain Information Management

Author: Zongmin Ma

Publisher: Springer

Published: 2013-03-30

Total Pages: 167

ISBN-13: 364237509X

DOWNLOAD EBOOK

This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.


Book Synopsis Advances in Probabilistic Databases for Uncertain Information Management by : Zongmin Ma

Download or read book Advances in Probabilistic Databases for Uncertain Information Management written by Zongmin Ma and published by Springer. This book was released on 2013-03-30 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.

Probabilistic Databases

Probabilistic Databases

Author: Dan Suciu

Publisher: Morgan & Claypool Publishers

Published: 2011-07-07

Total Pages: 182

ISBN-13: 1608456811

DOWNLOAD EBOOK

Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques


Book Synopsis Probabilistic Databases by : Dan Suciu

Download or read book Probabilistic Databases written by Dan Suciu and published by Morgan & Claypool Publishers. This book was released on 2011-07-07 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques

Advances in Databases and Information Systems

Advances in Databases and Information Systems

Author: Barbara Catania

Publisher: Springer Science & Business Media

Published: 2010-09-09

Total Pages: 614

ISBN-13: 3642155758

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 14th East European Conference on Advances in Databases and Information Systems, ADBIS 2010, held in Novi Sad, Serbia on September 20-24, 2010. The 36 revised full papers and 14 short papers were carefully selected from 165 submissions. Tolically the papers span a wide spectrum of topics in the database and information systems field, including database theory, advanced DBMS technologies, design methods, data mining and data warehousing, spatio-temporal and graph structured data and database applications.


Book Synopsis Advances in Databases and Information Systems by : Barbara Catania

Download or read book Advances in Databases and Information Systems written by Barbara Catania and published by Springer Science & Business Media. This book was released on 2010-09-09 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th East European Conference on Advances in Databases and Information Systems, ADBIS 2010, held in Novi Sad, Serbia on September 20-24, 2010. The 36 revised full papers and 14 short papers were carefully selected from 165 submissions. Tolically the papers span a wide spectrum of topics in the database and information systems field, including database theory, advanced DBMS technologies, design methods, data mining and data warehousing, spatio-temporal and graph structured data and database applications.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases

Author: Walter Daelemans

Publisher: Springer

Published: 2008-08-17

Total Pages: 714

ISBN-13: 3540874798

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.


Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Walter Daelemans

Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer. This book was released on 2008-08-17 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Information Modelling and Knowledge Bases XXV

Information Modelling and Knowledge Bases XXV

Author: IOS Press

Publisher: IOS Press

Published: 2014-01-14

Total Pages: 336

ISBN-13: 1614993610

DOWNLOAD EBOOK

Because of our ever increasing use of and reliance on technology and information systems, information modelling and knowledge bases continue to be important topics in those academic communities concerned with data handling and computer science. As the information itself becomes more complex, so do the levels of abstraction and the databases themselves. This book is part of the series Information Modelling and Knowledge Bases, which concentrates on a variety of themes in the important domains of conceptual modeling, design and specification of information systems, multimedia information modeling, multimedia systems, ontology, software engineering, knowledge and process management, knowledge bases, cross-cultural communication and context modeling. Theoretical disciplines, including cognitive science, artificial intelligence, logic, linguistics and analytical philosophy, also receive attention. The selected papers presented here cover many areas of information modeling and knowledge bases including: theory of concepts, semantic computing, data mining, context-based information retrieval, ontological technology, image databases, temporal and spatial databases, document data management, software engineering, cross-cultural computing, environmental analysis, social networks, WWW information management, and many others. This new issue also contains papers initiated by the panels on: “Cross-cultural Communication with Icons and Images” and “Conceptual Modelling of Collaboration for Information Systems”. The book will be of interest to all those interested in advances in research and applications in the academic disciplines concerned.


Book Synopsis Information Modelling and Knowledge Bases XXV by : IOS Press

Download or read book Information Modelling and Knowledge Bases XXV written by IOS Press and published by IOS Press. This book was released on 2014-01-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of our ever increasing use of and reliance on technology and information systems, information modelling and knowledge bases continue to be important topics in those academic communities concerned with data handling and computer science. As the information itself becomes more complex, so do the levels of abstraction and the databases themselves. This book is part of the series Information Modelling and Knowledge Bases, which concentrates on a variety of themes in the important domains of conceptual modeling, design and specification of information systems, multimedia information modeling, multimedia systems, ontology, software engineering, knowledge and process management, knowledge bases, cross-cultural communication and context modeling. Theoretical disciplines, including cognitive science, artificial intelligence, logic, linguistics and analytical philosophy, also receive attention. The selected papers presented here cover many areas of information modeling and knowledge bases including: theory of concepts, semantic computing, data mining, context-based information retrieval, ontological technology, image databases, temporal and spatial databases, document data management, software engineering, cross-cultural computing, environmental analysis, social networks, WWW information management, and many others. This new issue also contains papers initiated by the panels on: “Cross-cultural Communication with Icons and Images” and “Conceptual Modelling of Collaboration for Information Systems”. The book will be of interest to all those interested in advances in research and applications in the academic disciplines concerned.

AI 2001: Advances in Artificial Intelligence

AI 2001: Advances in Artificial Intelligence

Author: Mike Brooks

Publisher: Springer

Published: 2003-07-31

Total Pages: 678

ISBN-13: 3540456562

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 14th Australian Joint Conference on Artificial Intelligence, AI 2001, held in Adelaide, Australia, in December 2001. The 55 revised full papers presented together with one invited contribution were carefully reviewed and selected from a total of 100 submissions. The papers cover the whole range of artificial intelligence from theoretical and foundational issues to advanced applications in a variety of fields.


Book Synopsis AI 2001: Advances in Artificial Intelligence by : Mike Brooks

Download or read book AI 2001: Advances in Artificial Intelligence written by Mike Brooks and published by Springer. This book was released on 2003-07-31 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th Australian Joint Conference on Artificial Intelligence, AI 2001, held in Adelaide, Australia, in December 2001. The 55 revised full papers presented together with one invited contribution were carefully reviewed and selected from a total of 100 submissions. The papers cover the whole range of artificial intelligence from theoretical and foundational issues to advanced applications in a variety of fields.

AI 2001: Advances in Artificial Intelligence

AI 2001: Advances in Artificial Intelligence

Author: Markus Stumptner

Publisher: Springer Science & Business Media

Published: 2001-11-28

Total Pages: 678

ISBN-13: 3540429603

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 14th Australian Joint Conference on Artificial Intelligence, AI 2001, held in Adelaide, Australia, in December 2001. The 55 revised full papers presented together with one invited contribution were carefully reviewed and selected from a total of 100 submissions. The papers cover the whole range of artificial intelligence from theoretical and foundational issues to advanced applications in a variety of fields.


Book Synopsis AI 2001: Advances in Artificial Intelligence by : Markus Stumptner

Download or read book AI 2001: Advances in Artificial Intelligence written by Markus Stumptner and published by Springer Science & Business Media. This book was released on 2001-11-28 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th Australian Joint Conference on Artificial Intelligence, AI 2001, held in Adelaide, Australia, in December 2001. The 55 revised full papers presented together with one invited contribution were carefully reviewed and selected from a total of 100 submissions. The papers cover the whole range of artificial intelligence from theoretical and foundational issues to advanced applications in a variety of fields.

Modeling and Using Context

Modeling and Using Context

Author: Gábor Bella

Publisher: Springer Nature

Published: 2019-11-12

Total Pages: 253

ISBN-13: 3030349748

DOWNLOAD EBOOK

This book constitutes the proceedings of the 11th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2019, held in Trento, Italy, in November 2019. The 20 full papers and 4 invited talks presented were carefully reviewed and selected from 31 submissions. The papers feature research in a wide range of disciplines related to issues of context and contextual knowledge and discuss commonalities across and differences between the disciplines' approaches to the study of context. They cover a large spectrum of fields, including philosophy of language and of science, computational papers on context-aware information systems, artificial intelligence, and computational linguistics, as well as cognitive and social sciences.


Book Synopsis Modeling and Using Context by : Gábor Bella

Download or read book Modeling and Using Context written by Gábor Bella and published by Springer Nature. This book was released on 2019-11-12 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 11th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2019, held in Trento, Italy, in November 2019. The 20 full papers and 4 invited talks presented were carefully reviewed and selected from 31 submissions. The papers feature research in a wide range of disciplines related to issues of context and contextual knowledge and discuss commonalities across and differences between the disciplines' approaches to the study of context. They cover a large spectrum of fields, including philosophy of language and of science, computational papers on context-aware information systems, artificial intelligence, and computational linguistics, as well as cognitive and social sciences.