Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To thisend, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink. Hence, existing techniques postulate a hierarchical structure of the network that generates and processes the event data. This largely neglects the optimisation potential that stems from truly decentralised query evaluation with potentially many sinks. To address this gap, in this paper, we propose Multi-Sink Evaluation (MuSE) graphs as a formal computational model to evaluate common CEP queries in a decentralised manner. We further prove the completeness of query evaluation under this model. Striving for distributed CEP that can scale to large volumes of high-frequency event streams, we show how to reason on the network costs induced by distributed query evaluation and prune inefficient query execution plans. As such, our work lays the foundation for distributed CEP that is both, sound and efficient.

中文翻译：

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分布式复杂事件处理效率的推理

复杂事件处理（CEP）评估事件数据流上的查询以检测感兴趣的情况。如果事件数据是由地理分布的源产生的，则CEP可以利用网络内处理，该处理在网络的各个节点之间分配查询的评估。为此，将查询模块化，并将各个查询运算符分配给节点，尤其是充当数据源的节点。但是，用于这种操作员放置的现有解决方案受到限制，因为它们假定所有查询结果都在一个指定的节点（通常称为接收器）处收集。因此，现有技术假定生成和处理事件数据的网络的分层结构。这在很大程度上忽略了源于真正分散的查询评估（可能有很多接收器）的优化潜力。为了解决这一差距，在本文中，我们提出了多接收器评估（MuSE）图作为正式的计算模型，以分散的方式评估常见的CEP查询。我们进一步证明了该模型下查询评估的完整性。努力争取可以扩展到大量高频事件流的分布式CEP，我们展示了如何推理由分布式查询评估和修剪效率低下的查询执行计划引起的网络成本。因此，我们的工作为健全，高效的分布式CEP打下了基础。努力争取可以扩展到大量高频事件流的分布式CEP，我们展示了如何推理由分布式查询评估和修剪效率低下的查询执行计划引起的网络成本。因此，我们的工作为健全，高效的分布式CEP打下了基础。努力争取可以扩展到大量高频事件流的分布式CEP，我们展示了如何推理由分布式查询评估和修剪效率低下的查询执行计划引起的网络成本。因此，我们的工作为健全，高效的分布式CEP打下了基础。