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Optimizing Real-Time Complex Event Processing Through Parameter-Driven Selection Policy
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AI
DOI：10.1007/978-981-95-5722-6_15.png)
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Abstract
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Complex Event Processing (CEP) is a powerful technique for detecting event patterns within event streams. Traditional CEP matching methods rely on rigid selection policies that focus on the continuity of events in queries. However, real-world applications often generate event streams with varying time granularities. In such cases, rigid selection policies tend to produce either too many redundant matches or too few meaningful matches, lacking the flexibility required for fine-tuning according to specific application needs. To address this challenge, this paper introduces a novel complex event query that integrates a parameter-driven selection policy. This policy allows users to specify a parameter for an event instance e, which constrains the maximum number of matches that e can generate. Additionally, we propose a matching algorithm that supports this parameter-driven selection policy by leveraging potential domination relationships among complex event matches. Finally, we conduct experiments on both synthetic and real-world datasets to demonstrate the effectiveness and efficiency of the proposed methods.
Keywords:
Complex event processing
Selection policy
Event domination
Derivation parameter
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