This paper presents a system designed to handle real-time streaming and the prioritization of events in dynamic, high-volume data environments. The system efficiently processes incoming events and organizes them according to customizable prioritization rules, ensuring that high-priority events are addressed promptly. A standout feature of the system is its ability to support multiple prioritization rules simultaneously, offering the flexibility to modify or create new rules on the fly without disrupting the event processing pipeline. This adaptability makes the system particularly well-suited for real-time, data-driven environments such as IoT ecosystems, smart cities, industrial processes, and renewable energy systems, where the ability to prioritize and view events through different lenses is essential for operational efficiency and responsiveness. By dynamically adjusting to new rules and outputting rule-specific timelines, the system offers a powerful solution for managing and analyzing event streams in complex, rapidly evolving contexts.

DynaCEP a framework for Dynamic Event Streaming in IoT

Carchiolo V.;Malgeri M.;
2024-01-01

Abstract

This paper presents a system designed to handle real-time streaming and the prioritization of events in dynamic, high-volume data environments. The system efficiently processes incoming events and organizes them according to customizable prioritization rules, ensuring that high-priority events are addressed promptly. A standout feature of the system is its ability to support multiple prioritization rules simultaneously, offering the flexibility to modify or create new rules on the fly without disrupting the event processing pipeline. This adaptability makes the system particularly well-suited for real-time, data-driven environments such as IoT ecosystems, smart cities, industrial processes, and renewable energy systems, where the ability to prioritize and view events through different lenses is essential for operational efficiency and responsiveness. By dynamically adjusting to new rules and outputting rule-specific timelines, the system offers a powerful solution for managing and analyzing event streams in complex, rapidly evolving contexts.
2024
CEP: Complex Event Processing
Docker
Dynamic Complex Event Processing
Event Streaming
Kafka
PyFlink
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/717691
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