This work is a summarized view on the results of a one-year cooperation between Oracle Corp. and the University of Leipzig. The goal was to research the organization of relationships within multi-dimensional time-series data, such as sensor data from the IoT area. We showed in this project that temporal property graphs with some extensions are a prime candidate for this organizational task that combines the strengths of both data models (graph and time-series). The outcome of the cooperation includes four achievements: (1) a bitemporal property graph model, (2) a temporal graph query language, (3) a conception of continuous event detection, and (4) a prototype of a bitemporal graph database that supports the model, language and event detection.

翻译：这项工作总结了Oracle公司与Leipzig大学之间为期一年的合作成果,目的是研究多维时间序列数据(例如来自IoT地区的传感器数据)内的关系组织。我们在这个项目中显示,时间属性图和一些扩展部分是这一组织任务的首要选择,这一任务结合了两个数据模型(绘图和时间序列)的长处。合作的成果包括四个成就:(1)咬住时地产图模型,(2)时间图查询语言,(3)连续事件探测概念,(4)支持模型、语言和事件探测的咬住时地产图数据库原型。