Query Processing on Probabilistic Data: A Survey
Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database community and the AI community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey presents the main approaches developed in the literature, reconciling concepts developed in parallel by the two research communities. It starts with an extensive discussion of the main probabilistic data models and their relationships, followed by a brief overview of model counting and its relationship to probabilistic data. The monograph proceeds to discuss lifted probabilistic inference, a suite of techniques developed in parallel by the Database and AI communities for probabilistic query evaluation. It then provides a summary of query compilation, presenting some theoretical results highlighting limitations of various query evaluation techniques on probabilistic data. It ends with a brief discussion of some popular probabilistic data sets, systems, and applications that build on this technology.
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Query Processing on Probabilistic Data: A Survey
Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database community and the AI community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey presents the main approaches developed in the literature, reconciling concepts developed in parallel by the two research communities. It starts with an extensive discussion of the main probabilistic data models and their relationships, followed by a brief overview of model counting and its relationship to probabilistic data. The monograph proceeds to discuss lifted probabilistic inference, a suite of techniques developed in parallel by the Database and AI communities for probabilistic query evaluation. It then provides a summary of query compilation, presenting some theoretical results highlighting limitations of various query evaluation techniques on probabilistic data. It ends with a brief discussion of some popular probabilistic data sets, systems, and applications that build on this technology.
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Query Processing on Probabilistic Data: A Survey

Query Processing on Probabilistic Data: A Survey

by Guy Van den Broeck, Dan Suciu
Query Processing on Probabilistic Data: A Survey

Query Processing on Probabilistic Data: A Survey

by Guy Van den Broeck, Dan Suciu

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Overview

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database community and the AI community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey presents the main approaches developed in the literature, reconciling concepts developed in parallel by the two research communities. It starts with an extensive discussion of the main probabilistic data models and their relationships, followed by a brief overview of model counting and its relationship to probabilistic data. The monograph proceeds to discuss lifted probabilistic inference, a suite of techniques developed in parallel by the Database and AI communities for probabilistic query evaluation. It then provides a summary of query compilation, presenting some theoretical results highlighting limitations of various query evaluation techniques on probabilistic data. It ends with a brief discussion of some popular probabilistic data sets, systems, and applications that build on this technology.

Product Details

ISBN-13: 9781680833140
Publisher: Now Publishers
Publication date: 08/08/2017
Series: Foundations and Trends in Databases , #20
Pages: 162
Product dimensions: 6.14(w) x 9.21(h) x 0.35(d)

Table of Contents

1: Introduction 2: Probabilistic Data Model 3: Weighted Model Counting 4: Lifted Query Processing 5: Query Compilation 6: Data, Systems, and Applications 7: Conclusions and Open Problems. Acknowledgments. References
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