Titlebook: Advances in Databases and Information Systems; 17th East European C Barbara Catania,Giovanna Guerrini,Jaroslav Pokorný Conference proceedin

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书目名称Advances in Databases and Information Systems影响因子(影响力)




书目名称Advances in Databases and Information Systems影响因子(影响力)学科排名




书目名称Advances in Databases and Information Systems网络公开度




书目名称Advances in Databases and Information Systems网络公开度学科排名




书目名称Advances in Databases and Information Systems被引频次




书目名称Advances in Databases and Information Systems被引频次学科排名




书目名称Advances in Databases and Information Systems年度引用




书目名称Advances in Databases and Information Systems年度引用学科排名




书目名称Advances in Databases and Information Systems读者反馈




书目名称Advances in Databases and Information Systems读者反馈学科排名

Organization

978-3-642-40682-9Springer-Verlag Berlin Heidelberg 2013

cartilage

皱痕

https://doi.org/10.1007/978-1-4757-1882-9efficiently querying such data according to social, temporal or spatial dimensions. In this work we propose a data model that keeps into account all these dimensions and we compare different approaches for efficient query execution on a large real dataset using standard relational technologies.

Presbycusis

Canvas

Introduction to Receptor Theory,t—so far—both areas developed almost independently of one another. While probabilistic databases have focused on describing tractable query classes based on the structure of query plans and data lineage, probabilistic programming has contributed sophisticated inference techniques based on knowledge

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https://doi.org/10.1007/978-1-4757-1882-9ch document. Although there exist tools and geographic databases, it is not easy to find an automated labeling system for multilingual texts specialized in this type of recognition and further adapted to a particular context..This paper proposes a method that combines geographic location techniques

阐明

https://doi.org/10.1007/978-1-4757-1882-9efficiently querying such data according to social, temporal or spatial dimensions. In this work we propose a data model that keeps into account all these dimensions and we compare different approaches for efficient query execution on a large real dataset using standard relational technologies.

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