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Building Graphical Models from Relational Databases for Context-Aware Querying

机译:从关系数据库构建图形模型以进行上下文感知查询

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Context-aware plays an important role in modern society, especially to preference queries for user preferences depend on user current contexts. Popular mobile devices such as GPS, sensors and RFIDs produce context information to facilitate user right information acquisition. Due to that relational database is a powerful and sophisticated tool to manage large amount of data efficiently, context information can be taken as data items stored in the relational databases. To evaluate context-aware queries, graphical models are built from these relational data thus context-aware queries can be realized to query the constructed graphical models. To build a graphical model, user-defined methods are time-consuming and not efficient. To solve this problem, sophisticated database techniques are thoroughly exploited as well as synthesizing powerful ideas for defining probability distributions over relational domains to learn the graphical models, then query the learned models to realize context-awareness.
机译:背景信息在现代社会中发挥着重要作用,尤其是偏好用户偏好的查询取决于用户当前上下文。流行的移动设备,如GPS,传感器和RFID产生上下文信息,以便于用户右信息获取。由于关系数据库是一种强大而复杂的工具,可以有效地管理大量数据,因此可以将上下文信息作为存储在关系数据库中的数据项。为了评估上下文感知查询,图形模型由这些关系数据构建,因此可以实现上下文感知查询来查询构造的图形模型。要构建图形模型,用户定义的方法是耗时且不高效的。为了解决这个问题,已经彻底利用了复杂的数据库技术,并合成了用于定义关系域上的概率分布来学习图形模型的强大思想,然后查询学习模型以实现上下文意识。

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