Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things

The rapid advancement of artificial intelligence (AI) technologies has revolutionized the way we process and analyze data, particularly in the field of complex event processing, such as video query analysis. Traditional CEP systems often struggle with the dynamic demands of modern applications such as real-time or near realtime video analytics that require the integration of diverse data sources, for example, thousands of surveillance cameras deployed in a city, leading to limitations in their performance and applicability. Modern CEP pipelines are domain-specific and often struggle to adapt to dynamic changes in the environment in a timely manner. State-of-the-art applications (such as live video streaming on TikTok, YouTube etc.) generate an increasing volume of diverse, complex data that needs to be handled in the appropriate manner depending on the use case. Large Language Models (LLMs), also known as foundation models, inherently possess the ability to handle and analyze dynamic forms of data and therefore provide the necessary foundation upon which a dynamic CEP pipeline can be created which can support a diverse range of domains.