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.
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.
The Semantic Web, which is intended to establish a machine-understandable Web, is currently changing from being an emerging trend to a technology used in complex real-world applications. A number of...
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...
On various examples ranging from geosciences to environmental sciences, thisbook explains how to generate an adequate description of uncertainty, how to justifysemiheuristic algorithms for processing...
Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and...
This book is an anthology of the results of research and development in database query processing during the past decade. The relational model of data provided tremendous impetus for research into...
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