Stream reasoning systems are designed for complex decision-making from
possibly infinite, dynamic streams of data. Modern approaches to stream
reasoning are usually performing their computations using stand-alone solvers,
which incrementally update their internal state and return results as the new
portions of data streams are pushed. However, the performance of such
approaches degrades quickly as the rates of the input data and the complexity
of decision problems are growing. This problem was already recognized in the
area of stream processing, where systems became distributed in order to
allocate vast computing resources provided by clouds. In this paper we propose
a distributed approach to stream reasoning that can efficiently split
computations among different solvers communicating their results over data
streams. Moreover, in order to increase the throughput of the distributed
system, we suggest an interval-based semantics for the LARS language, which
enables significant reductions of network traffic. Performed evaluations
indicate that the distributed stream reasoning significantly outperforms
existing stand-alone LARS solvers when the complexity of decision problems and
the rate of incoming data are increasing. Under consideration for acceptance in
Theory and Practice of Logic Programming.

本文提出了一种分布式流推理方法，可以在不同解算器之间分配计算并通过数据流通信的方式高效地处理决策问题。在 LARS 语言中提出一种基于间隔的语义，以对网络流量进行显着降低，并在评估中明确表明，分布式流推理在处理复杂的决策问题和不断增长的数据率时明显优于现有的独立 LARS 解算器。