We study stream reasoning in $\text{DatalogMTL}$—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to forward-propagating $\text{DatalogMTL}$ programs, in which propagation of derived information towards past time points is precluded. Memory consumption in our generic algorithm depends both on the properties of the rule set and the input data stream; in particular, it depends on the distances between timestamps occurring in data. This may be undesirable in certain practical scenarios since these distances can be very small, in which case the algorithm may require large amounts of memory. To address this issue, we propose a second algorithm, where the size of the required memory becomes independent on the timestamps in the data at the expense of disallowing punctual intervals in the rule set. We have implemented our approach as an extension of the $\text{DatalogMTL}$ reasoner MeTeoR and tested it experimentally. The obtained results support the feasibility of our approach in practice.

我们研究流推理$\text{数据日志MTL}$— Datalog 的扩展，带有度量时间运算符。我们提出了一种适用于前向传播的完善的流推理算法$\text{数据日志MTL}$程序，其中排除了派生信息向过去时间点的传播。我们的通用算法中的内存消耗取决于规则集的属性和输入数据流；特别是，它取决于数据中出现的时间戳之间的距离。这在某些实际场景中可能是不希望的，因为这些距离可能非常小，在这种情况下算法可能需要大量内存。为了解决这个问题，我们提出了第二种算法，其中所需内存的大小变得独立于数据中的时间戳，但代价是不允许规则集中的准时间隔。我们已经实施了我们的方法作为$\text{数据日志MTL}$reasoner MeTeoR 并通过实验对其进行了测试。获得的结果支持我们的方法在实践中的可行性。