An Unsupervised Rule Generation Approach for Online Complex Event Processing

Petersen, Erick; Rlict, Marco Antonio To; Maag, Stéphane; Yamga, Thierry

doi:10.1109/nca.2018.8548210

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Petersen et al [31] extracted CEP rules from unlabeled data by combining the Xmeans clustering method with the SVM classification algorithm. The authors applied this approach to a real-world data set to demonstrate that their method works.…”

Section: Related Workmentioning

confidence: 99%

An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting

Liu

Cong

et al. 2022

Energies

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Energy is at the center of human society and drives the technologies and overall human well-being. Today, artificial intelligence (AI) technologies are widely used for system modeling, prediction, control, and optimization in the energy sector. The internet of things (IoT) is the core of the third wave of the information industry revolution and AI. In the energy sector, tens of billions of IoT appliances are linked to the Internet, and these appliances generate massive amounts of data every day. Extracting useful information from the massive amount of data will be a very meaningful thing. Complex event processing (CEP) is a stream-based technique that can extract beneficial information from real-time data through pre-establishing pattern rules. The formulation of pattern rules requires strong domain expertise. Therefore, at present, the pattern rules of CEP still need to be manually formulated by domain experts. However, in the face of complex, massive amounts of IoT data, manually setting rules will be a very difficult task. To address the issue, this paper proposes a CEP rule auto-extraction framework by combining deep learning methods with data mining algorithms. The framework can automatically extract pattern rules from unlabeled air pollution data. The deep learning model we presented is a two-layer LSTM (long short-term memory) with an attention mechanism. The framework has two phases: in the first phase, the anomalous data is filtered out and labeled from the IoT data through the deep learning model we proposed, and then the pattern rules are mined from the labeled data through the decision tree data mining algorithm in the second phase. We compare other deep learning models to evaluate the feasibility of the framework. In addition, in the rule extraction stage, we use a decision tree data mining algorithm, which can achieve high accuracy. Experiments have shown that the framework we proposed can effectively extract meaningful and accurate CEP rules. The research work in this paper will help support the advancement of the sector of air pollution prediction, assist in the establishment of air pollution regulatory strategies, and further contribute to the development of a green energy structure.

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Section: Related Workmentioning

confidence: 99%

An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting

Liu

Cong

et al. 2022

Energies

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“…The investigated classifiers classify the data elements in the data streams into different classes. Petersen et al [35] propose a hybrid approach of rule-based classifiers and kmeans algorithm that can derive labels for unlabelled data on the one hand and automatically generate CEP rules on the other. Similar to [35], the ARECEP framework [27] tackles data labeling and automatic rule extraction together.…”

Section: Machine Learning Of Cep Rulesmentioning

confidence: 99%

Bat4CEP: a bat algorithm for mining of complex event processing rules

Bruns

Dunkel

2022

Appl Intell

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Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel bat-inspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.

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