Active Databases and Temporal Reasoning

对信息服务的爆炸性需求正在推动数据库朝着专门的、往往不兼容的方法发展。然而，应对每个子领域的艰巨技术挑战的最佳解决方案往往是来自不同方法的技术和模型的汇聚。例如，先进的主动数据库系统支持通过复杂的时态事件来激活规则，而时态事件的复杂模式一直是用于时间序列分析和时态查询的语言的焦点。预计这些研究领域的融合将带来显著的好处。此外，用时间进行推理的能力对于主动数据库的动态建模和避免不受欢迎的规则行为(如非终止性)至关重要。这个项目追求一种统一的方法来设计和优化用于时间序列分析、数据库的时态推理和带有复合事件的主动规则的语言。此外，该项目正在为主动数据库规则开发更有效的时态语义，并提供预测和控制其行为的方法和工具。这项研究将为广泛的政府和工业应用提供能力，这些应用必须对时态数据进行推理和管理。

An exploding demand for information services is pushing databases toward specialized and often incompatible approaches. Yet, the best solutions to the formidable technical challenges of each sub-field are often found in the convergence of techniques and models from different approaches. For instance, advanced active database systems support rule activation by complex temporal events, while complex patterns of temporal events have been the focus of languages for time-series analysis and temporal queries. Significant benefits are expected from the confluence of these areas of research. Moreover, the capability of reasoning with time is critical to model the dynamics of active databases, and to avoid undesirable rule behavior, such as non-termination. This project pursues a unified approach to the design and query optimization of languages for time-series analysis, temporal reasoning on databases, and active rules with composite events. In addition, the project is developing more effective temporal semantics for active database rules, and providing methods and tools to predict and control their behavior. This research will provide capabilities for a wide range of government and industrial applications that must reason with and manage temporal data.