.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
Complex event processing (CEP) matches patterns over a continuous stream of events to detect situations of interest. Yet, the definition of an event pattern that precisely characterises a particular situation is challenging: there are manifold dimensions to correlate events, including time windows and value predicates. In the presence of historic event data that is labelled with the situation to detect, event patterns can be learned automatically. To cope with the combinatorial explosion of pattern candidates, existing approaches work on a type-level and discover patterns based on predefined event abstractions, aka event types. Hence, discovery is limited to patterns of a fixed granularity and users face the burden to manually select appropriate event abstractions.
We present IL-M
iner
, a system that discovers event patterns by genuinely working on the instance-level, not assuming a priori knowledge on event abstractions. In a multi-phase process, IL-M
iner
first identifies relevant abstractions for the construction of event patterns. The set of events explored for pattern discovery is thereby reduced, while still providing formal guarantees on correctness, minimality, and completeness of the discovery result. Experiments using real-world datasets from diverse domains show that IL-M
iner
discovers a much broader range of event patterns compared to the state-of-the-art in the field.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
暂无期刊信息
机构
暂无机构信息
引用论文
暂无论文信息