Supporting Multiple Time Granularities in Query Evaluation and Data Mining

这个项目的目标是提供一个灵活的框架来支持多个时间粒度。 该框架的目标用途是自动评估用户查询和发现时间模式（即，数据挖掘），其中用户查询或时间模式涉及与所存储数据的粒度不匹配的粒度。其基本思想是添加必要的功能，以便数据库系统能够理解和推理涉及多个时间粒度的信息。 有效地评估用户查询和发现时间模式的算法也将进行研究，并将建立一个实验原型。 这项研究的影响应该是双重的：首先，所提出的框架应该提供基本的工具，数据库系统，以支持灵活的用户查询，这将使用户从不必要的任务，编写复杂的查询或改变他们的模式描述，以适应特定的粒度用于存储时态数据在数据库中。其次，探索的优化技术应该提供有效的评估查询和发现的时间模式的策略和算法。拟议的实验工作应提供深入了解的主题和所提出的理论和方法的实用性。

The objective of this project is to provide a flexible framework for supporting multiple time granularities. The targeted use of the framework is for automatic evaluation of user queries and for discovering temporal patterns (i.e., data mining) in an environment where either the user queries or temporal patterns involve granularities that do not match the granularity of the stored data. The basic idea is to add the necessary functionalities so that the database system is able to understand and reason about information involving multiple time granularities. Algorithms for efficiently evaluating user queries and discovering temporal patterns will also be investigated, and an experimental prototype will be built. The impact of this research should be twofold: Firstly, the proposed framework should provide the basic tools for database systems to support flexible user queries, which would free users from the unnecessary tasks of writing complex queries or changing their pattern descriptions to fit the specific granularity used to store temporal data in the database. Secondly, the optimization techniques explored should provide strategies and algorithms for efficient evaluations of queries and discovery of temporal patterns. The proposed experimental work should provide insights into the subject and into the practicality of the proposed theory as well as methods.