Complex Event Processing for City Officers: A Filter and Pipe Visual Approach

Bonino, Dario; Russis, Luigi De

doi:10.1109/jiot.2017.2728089

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…These systems can filter and aggregate events to provide semantic materials and constitute high-level meaningful events. Most of the existing CEP engines, such as knowledge-infused CEP [77], context-aware nested CEP [78], model4CEP [79], and intelligent machine to machine [80], do not present any solution for reducing or handling uncertainty in complex event detection.…”

Section: Discussionmentioning

confidence: 99%

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Honarparvar

Saeedi

Liang

et al. 2021

IJGI

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Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models.

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Section: Discussionmentioning

confidence: 99%

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Honarparvar

Saeedi

Liang

et al. 2021

IJGI

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“…Complex Event Processing (CEP) is an an emerging reference framework and standard for building and managing event-driven information systems [20,21]. The goal is to get the meaningful complex events by reasoning and analyzing the event data flow, and respond in real-time.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analyzing User Behavior Risks in Online Shopping Processes Based on Data-Driven and Petri-Net Methods

Zhai

et al. 2023

cai

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With the rapid spread of e-commerce and e-payment, the increasing number of people choose online shopping instead of traditional buying way. However, the malicious user behaviors have a significant influence on the security of users' accounts and property. In order to guarantee the security of shopping environment, a method based on Complex Event Process (CEP) and Colored Petri nets (CPN) is proposed in this paper. CEP is a data-driven technology that can correlate and process a large amount of data according to Event Patterns, and CPN is a formal model that can simulate and verify the specifications of the online shopping processes. In this work, we first define the modeling scheme to depict the user behaviors and Event Patterns of online shopping processes based on CPN. The Event Patterns can be constructed and verified by formal methods, which guarantees the correctness of Event Patterns. After that, the Event Patterns are translated into Event Pattern Language (EPL) according to the corresponding algorithms. Finally, the EPLs can be inserted into the complex event processing engine to analyze the users' behavior flows in real-time. In this paper, we validate the effectiveness of the proposed method through case studies.

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“…CEP is employed in many domains such as in big data analysis [2] [3], business process management [4], smart cities and internet of things [5], [6]. In such domains, a CEP system must process high rates of input events to detect high number of complex patterns using the priori specified processing rules.…”

Section: Introductionmentioning

confidence: 99%

Scalable Complex Event Processing Using Rule Distribution

Sharifi

Fardbastani

2018

AzJHPC

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Complex event processing (CEP) systems are currently widely used in large-scale enterprises for the processing of high and dynamically changing rates of input events using large number of complex rules. Given the hardware limitations of vertically scaled CEP solutions, horizontal scalability has become an essential requirement for modern CEP systems. In this paper, we propose an adaptive load-balancing technique via rule distribution (called ARD) for a cluster of CEP engines that provides horizontal scalability for CEP systems. Our experiments show our proposed technique provides higher scalability and yields higher throughput in comparison with two previously proposed non-adaptive load-balancing techniques, namely VISIRI and SCTXPF, when the system faces with variable workload. In addition, ARD keeps the system balanced more often.

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Complex Event Processing for City Officers: A Filter and Pipe Visual Approach

Cited by 6 publications

References 23 publications

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Modeling and Analyzing User Behavior Risks in Online Shopping Processes Based on Data-Driven and Petri-Net Methods

Scalable Complex Event Processing Using Rule Distribution

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