一种基于图模型的Web数据库采样方法

Web数据库中,海量的信息隐藏在具有特定查询能力的查询接口后面,使人无法了解一个Web数据库内容的特征,比如主题的分布、更新的频率等,这就为DeepWeb数据集成带来了巨大的挑战.为了解决这个问题,提出了一种基于图模型的Web数据库采样方法,可以通过查询接口从Web数据库中以增量的方式获取近似随机的样本,即每次查询获取一定数量的样本记录,并且利用已经保存在本地的样本记录生成下一次的查询.该方法的一个重要特点是不受查询接口中属性表现形式的局限,因此是一种一般的Web数据库采样方法.在本地的模拟实验和真实Web数据库上的大量实验表明,该方法可以在较小代价下获得高质量的样本.

A Graph-Based Approach for Web Database Sampling

A flood of information is hidden behind the Web-based query interfaces with specific query capabilities, which makes it difficult to capture the characteristics of the Web database, such as the topic and the frequency of updates. This poses a great challenge for Deep Web data integration. To address this problem, a graph-based approach WDB-Sampler for Web database sampling is proposed in this paper, which can incrementally obtain sample records from a Web database through its query interface. That is, a number of samples are obtained for the current query, and one of them is transformed into the next query. The important characteristic of this approach is it can adapt to different kinds of attributes on the query interfaces. The extensive experiments on the local simulation Web databases and the real Web databases prove that the approach can achieve high-quality samples from a Web database at a lower cost.