Query Approximation Techniques for Databases with Time Constraints and Incomplete Information

目前的数据库系统处理与一个 在给出答案之前先询问。 因此， 要处理的数据对于时间受限变得过多（并且， 可能是实时）数据库应用环境。 为 例如，对于科学数据库应用程序，数据收集和 如果有真实的- 时间/在线查询功能， 检查生成的科学数据的质量。 此外，通过实时/在线“即时”数据分析（查询） 能力，科学数据库中存储数据的大小 可以保持小规模，因此易于管理。 类似的问题时有发生 在时间受限的制造数据库应用中。 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。 到 为此，可以使用数据样本并利用统计 近似构造对给定的近似响应 查询. 在这项研究中，将开发一种方法， 数据库近似查询响应的构造 在时间（和/或准确性）限制下， 数据

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.