With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined rules mostly created by experts. However, especially in heterogeneous IoT environments, it may become extremely hard to define these rules manually due to complex and dynamic nature of data. Defining rules requires accurate knowledge of relevant events by utilizing temporal dependencies and relations among attributes of events. Therefore, there is a need for an automatic rule extraction system which is capable of generating rules automatically from unlabeled IoT data. This paper proposes a generalized framework for automatic CEP rule extraction with the help of deep learning (DL) methods. The proposed framework contains two main phases which are data labeling and automatic rule extraction phases. We compare several DL methods with each other and regression-based methods to evaluate the proposed framework. In addition, several rule mining methods are employed in the automatic rule extraction phase in a comparative manner. The reconstruction error and prediction success rate of our framework are evaluated using an air quality dataset collected from a smart city application. The results indicate that the proposed framework is able to generate meaningful and accurate rules for unlabeled IoT data.

随着物联网（IoT）的最新发展，生成具有高速度，多样性和大容量原始数据的传感器的数量已大大增加。通过使用复杂事件处理（CEP）系统，可以从原始数据中提取有价值的信息，并将其用于进一步的应用。CEP是一种流处理技术，通过主要由专家创建的预定义规则将原子事件与复杂事件匹配。但是，尤其是在异构物联网环境中，由于数据的复杂性和动态性，手动定义这些规则可能变得非常困难。定义规则需要通过利用时间依赖性和事件属性之间的关系来准确了解相关事件。因此，需要一种自动规则提取系统，该系统能够从未标记的IoT数据自动生成规则。本文提出了一种借助深度学习（DL）方法自动提取CEP规则的通用框架。提议的框架包含两个主要阶段，分别是数据标记阶段和规则自动提取阶段。我们将几种DL方法以及基于回归的方法进行比较，以评估所提出的框架。另外，在自动规则提取阶段以比较的方式采用了几种规则挖掘方法。我们使用从智能城市应用程序收集的空气质量数据集来评估我们框架的重建误差和预测成功率。