Active Databases and Temporal Reasoning

对信息服务的爆炸性需求正在推动数据库向专业化和常常不兼容的方向发展。然而，对每个子领域的艰巨技术挑战的最佳解决方案通常是在不同方法的技术和模型的融合中找到的。例如，高级活动数据库系统支持通过复杂的时间事件激活规则，而时间事件的复杂模式一直是时间序列分析和时间查询语言关注的焦点。这些研究领域的融合有望带来显著的效益。此外，随时间推理的能力对于活动数据库的动态建模和避免不期望的规则行为（如不终止）至关重要。该项目追求统一的方法来设计和查询优化语言的时间序列分析，数据库上的时间推理，与复合事件的活动规则。此外，该项目正在为活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，这些应用必须对时间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.