SGER: Data Analytics over Hidden Databases

结构化隐藏数据库在Web上广泛流行。它们提供受限的类似表单的搜索界面，其允许用户通过指定所寻求的元组的期望属性值来执行搜索查询，并且系统通过返回一些（例如，top-k）满足选择条件的元组，由合适的排序函数排序。尽管隐藏数据库的搜索界面是考虑到集中搜索查询而设计的，但是对于某些应用，从搜索查询的返回结果推断数据的更聚合的视图可能是有利的。这些汇总的信息将有助于学习数据分布或构建挖掘模型，然后可以用于支持和优化众多新兴的数据分析应用程序。这项研究涉及开发有效的技术来执行数据分析，特别是采样，通过其公共接口隐藏的结构化数据库。结果包括用于对具有异构混合数据类型的隐藏数据库进行采样的高效算法，用于对不同类型的搜索接口进行采样的可验证性结果，以及演示真实世界隐藏数据库采样的原型工具集。对隐藏数据库进行高级分析查询的能力是各种公司、政府和安全机构中的知识工作者所需要的。该项目的部分内容将被纳入教学，并由学生作为高级课程项目的一部分进行，这将有可能吸引有动力的学生攻读博士学位。项目网址（http：//dbxlab.uta.edu/dataAnalytics.html）将用于传播成果。

Structured hidden databases are widely prevalent on the Web. They provide restricted form-like search interfaces that allow users to execute search queries by specifying desired attribute values of the sought-after tuples, and the system responds by returning a few (e.g., top-k) tuples that satisfy the selection conditions, sorted by a suitable ranking function. Although search interfaces for hidden databases are designed with focused search queries in mind, for certain applications it may be advantageous to infer more aggregated views of the data from the returned results of search queries. Such aggregated information will facilitate learning data distributions or building mining models, which can then be used to power and optimize a multitude of emerging data analytical applications. This research involves developing effective techniques for performing data analytics, especially sampling, over hidden structured databases via their public interfaces. The outcomes include efficient algorithms for sampling hidden databases with a heterogeneous mix of data types, achievability results for sampling different types of search interfaces, and a prototypical toolset which demonstrates the sampling of real-world hidden databases. The ability to pose high-level analytical queries over hidden databases is needed by knowledge workers in a wide variety of corporations, governments, and security agencies. Parts of this project will be integrated into teaching and carried out by students as part of advanced class projects, which will potentially attract motivated students to pursue doctoral degrees. The project Web site (http://dbxlab.uta.edu/dataAnalytics.html) will be used for results dissemination.