Query Approximation Techniques for Databases with Time Constraints and Incomplete Information

目前的数据库系统在给出响应之前处理与查询相关的所有数据。因此，对于时间受限(可能是实时)的数据库应用程序环境，要处理的数据的大小变得过大。例如，对于科学数据库应用程序，如果有实时/在线查询能力，返回近似响应以检查生成的科学数据的质量，则数据收集和分析能力可以大大增强。此外，利用实时/在线“即时”数据分析(查询)能力，可以将科学数据库中存储的数据的大小保持在较小的水平，从而使其易于管理。类似的问题也出现在时间受限的制造数据库应用程序中。需要一种新的方法来系统地减少处理查询所涉及的数据的时间。为此，可以使用数据样本并利用统计近似来构建对给定查询的近似响应。在这项研究中，将开发一种方法，用于在时间(和/或精度)约束下和数据不完整的情况下构建数据库的近似查询响应。

Present database systems process all the data related to a query before giving out responses. As a result, the size of the data to be processed becomes excessive for time-constrained (and, perhaps, realtime) database application environments. For example, for Scientific database applications, data gathering and analysis capabilities can be greatly enhanced if there are real- time/online querying capabilities that return approximate responses to check the quality of the generated scienctific data. Also, with real-time/online "on-the-fly" data analysis (querying) capabilities, the size of the stored data in scientific databases can be kept small and, thus, manageable. Similar problems occur in time-constrained manufacturing database applications. A new methodology is needed to cut down systematically the time to process the data involved in processing the query. To this end, one can use data samples and utilize statistical approximation to construct an approximate response to given query. In this research, a methodology will be developed for the construction of approximate query responses for databases under time (and/or accuracy) constraints, and with incomplete data.