An online learning based approach for CEP rule generation

Petersen, Erick; To, Marco Antonio; Maag, Stéphane

doi:10.1109/latincom.2016.7811563

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Several studies have addressed the significance of evaluating the complexity issue of complex pattern queries and its impact on the overall performance of CEP systems [166,169,174]. These studies reveal the relationship between pattern complexity and the increased processing cost of CEP queries.…”

Section: Complexity Verses Processing Costmentioning

confidence: 99%

“…Towards minimizing the impact of increased pattern complexity, several CEP related studies have focused either on reducing the number of CEP detection rules to maximize throughput and minimize latency [31,50,62,167] or optimizing CEP query plans with available computational resources [168,169]. Other research efforts have focused on developing cost models to estimate the cost of using computational resources while running CEP queries [166,168,170].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A situation refinement model for complex event processing

Alakari

Gebali

2020

Knowledge-Based Systems

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Section: Complexity Verses Processing Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A situation refinement model for complex event processing

Alakari

Gebali

2020

Knowledge-Based Systems

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“…Bhargavi et al [19], proposed an adaptive CEP engine that can update its Knowledge Base at run-time in order to make it more dynamic and interactive. Petersen et al [23] in their work have presented a CEP rule alteration engine using Online Learning techniques, thus making the engine more efficient over time, since the rules would adapt better to the current state of the environment.…”

Section: Complex Event Processingmentioning

confidence: 99%

Towards the Identification of Context in 5G Infrastructures

Symvoulidis

Tsoumas

Kyriazis

2019

Advances in Intelligent Systems and Computing

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The evolution of communication networks, bringing the fifth generation (5G) of mobile communications in the foreground, gives the vertical industries opportunities that were not possible until now. Flexible network and computing infrastructure management can be achieved, hence bringing more freedom to the service providers, to maximize the performance of their computing resources. Still, challenges regarding the orchestration of these resources may arise. For this reason, an engine that can recognize possible factors that affect the use of these resources and come up with solutions when needed in real-time, is required. In this paper we present a novel Complex Event Processing that is enriched with Machine Learning capabilities in order to be fully adaptive to its environment, as a solution for monitoring application components deployed in 5G infrastructures. The proposed engine utilizes Incremental DBSCAN to identify the normal behavior of the deployed services and adjust the rules accordingly.

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An online learning based approach for CEP rule generation

Cited by 2 publications

References 18 publications

A situation refinement model for complex event processing

A situation refinement model for complex event processing

Towards the Identification of Context in 5G Infrastructures

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