Rule Extraction using Machine Learning Classifiers for Complex Event Processing

Kumar, Shashi Shekhar; Kumar, Ashutosh; Chandra, Ritesh; Agarwal, Sonali; Syafrullah, Mohammad; Adiyarta, Krisna

doi:10.1109/eecsi59885.2023.10295840

Cited by 2 publications

(1 citation statement)

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“…• Machine Learning: The model can be trained on historical data to personalize risk scores and dynamically adjust based on certified parameters (Kumar, Kumar, et al, 2023a). Rules can be ingested in the CEP system for individual patients based on their unique medical history and risk factors.…”

Section: Case Studymentioning

confidence: 99%

Rule based complex event processing for IoT applications: Review, classification and challenges

Kumar,

Agarwal

2024

Expert Systems

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In recent decades, data aggregation and correlation have emerged as a significant and challenging area of research for finding useful relationships between different features of streaming data. Similarly, complex event processing (CEP) has emerged as a vital tool for aggregating and finding patterns from streaming data such as video streaming, real‐time stock trades, sensor data and more. In this context, rule‐based classifier algorithms are widely employed to discover valuable patterns from streaming data and cause‐and‐effect relationships between events. However, streaming data is dynamic and complex in nature, and it is not feasible to update the rules manually by domain experts continuously. On the contrary, dynamic data sometimes fails to adopt rules, which leads to sub optimal CEP implementation. Rule‐based CEP systems come up with their own set of challenges, which primarily includes rules adaptability for streaming IoT data. In dynamic environment, shifting patterns in data streams can impact the performance of the CEP system, as well as the correlation between events, and finding useful patterns is very much challenging. In this article, our objective is to provide a comprehensive survey for CEP using rule‐based algorithms applied across various domain and applications. We present a broad literature review using a sequential expansion of methods used for CEP, which primarily include event producers, preprocessing of events, robust rule implementation, and decision support. Additionally, we present a detailed classification of approaches used for different applications. Finally, after applying the rigorous review, it was feasible to present the state‐of‐the‐art research, which focuses on designing robust rules, application specific insights, scalable event‐driven architectures and finding the domain's trends with significant challenges and future scope.

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Section: Case Studymentioning

confidence: 99%