Efficient Adaptive Detection of Complex Event Patterns

Kolchinsky, Ilya; Schuster, Assaf

doi:10.48550/arxiv.1801.08588

Cited by 1 publication

(3 citation statements)

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“…The performance evaluation of three processing approaches, proposed approach, Instans [50], ESPER [16], NFA-HTS [40] and LCA [43]. A slight modification has been made to these algorithms to solve the problems.…”

Section: Resultsmentioning

confidence: 99%

“…• We propose a novel uncertain event detection algorithm (UDA), that is based on the hashing technique and thread pool technique to detect uncertain patterns in RFID CEP in the distributed system. • Comprehensive overall performance evaluation of the proposed approach is carried out and in comparison with Instans [15], ESPER [16], NFA-HTS [40] and LCA [43].…”

Section: Sensing Devicesmentioning

confidence: 99%

“…The proposed approach initiates an event query tree and detects homogeneous and unnecessary operations by propagating the tree and hash table to detect complex events. According to [43], lazy chain automata were proposed to detect complex events. This approach mainly utilizes the NFA and lazy evaluation model to detect the frequency order of the event types in stock exchange.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Approach for Uncertain Event Detection in RFID Complex Event Processing

Yaacob

Mahdin

Jawad

et al. 2021

JOIV : Int. J. Inform. Visualization

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The globalization of manufacturing has increased the risk of counterfeiting as the demand grows, the production flow increases, and the availability expands. The intensifying counterfeit issues causing a worriment to companies and putting lives at risk. Companies have ploughed a large amount of money into defensive measures, but their efforts have not slowed counterfeiters. In such complex manufacturing processes, decision-making and real-time reactions to uncertain situations throughout the production process are one way to exploit the challenges. Detecting uncertain conditions such as counterfeit and missing items in the manufacturing environment requires a specialized set of technologies to deal with a flow of continuously created data. In this paper, we propose an uncertain detection algorithm (UDA), an approach to detect uncertain events such as counterfeit and missing items in the RFID distributed system for a manufacturing environment. The proposed method is based on the hashing and thread pool technique to solve high memory consumption, long processing time and low event throughput in the current detection approaches. The experimental results show that the execution time of the proposed method is averagely reduced 22% in different tests, and our proposed method has better performance in processing time based on RFID event streams.

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Section: Resultsmentioning

confidence: 99%

Section: Sensing Devicesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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