A lightweight and extensible Complex Event Processing system for sense and respond applications

Zappia, Ivan; Paganelli, Federica; Parlanti, David

doi:10.1016/j.eswa.2012.01.197

Cited by 31 publications

(28 citation statements)

References 29 publications

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“…Another direction for future work would be to extend the proposed approach by exploiting information about user habits and daily activity patterns that can be extracted from sensors observation data through complex event processing techniques [38,39].…”

Section: Discussionmentioning

confidence: 99%

Appliance Recognition in an OSGi-Based Home Energy Management Gateway

Paganelli

Paradiso

Turchi

et al. 2015

International Journal of Distributed Sensor Networks

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The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing

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Section: Discussionmentioning

confidence: 99%

Appliance Recognition in an OSGi-Based Home Energy Management Gateway

Paganelli

Paradiso

Turchi

et al. 2015

International Journal of Distributed Sensor Networks

Self Cite

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“…The extraction process from primitive event to basic event can be described by the pseudo code shown in Algorithm 1. / / represent the material number (4) if the object was first read by reader 1 at 1 //Reader 1 represent the entrance reader of the process line (5) then Create event BE 0,1 = ( .ID, 0 , 1(ts), launch) //New product launch (6) else if the object was last read by reader at 2 //Reader represent the exit reader of the process line (7) then Create event BE ,4 = ( .ID, , 2(te), finish) //The product is finished (8) else the object was read by reader at 3 //The reader in the process line (9) if first read (10) then Create event BE ,1 = ( .ID, , 3(ts), infield) //The infield event (11) and BE ,2 = ( .ID, −1 , 1(ts), 3(te), stay) //The stay event (12) and BE ,3 = ( .ID, −1 , 3(te), outfield) //The outfield event (13) Else Create event BE ,2 = ( .ID, −1 , 1(ts), 3( now), stay) //The stay event Algorithm 1: The extraction process from primitive event to basic event.…”

Section: Extraction Process Of Critical Eventmentioning

confidence: 99%

“…Fang et al [11] presented an event-driven shop-floor work in progress (WIP) management platform for creating a ubiquitous manufacturing (UM) environment to process the huge amount of RFID data into useful information for managerial uses. Zappia et al [12] proposed a lightweight and extensible complex event processing system based on a layered architectural design to extract meaningful events from raw data streams originated by sensing infrastructures. Li et al [13] presented a hybrid method of mixture of Gaussian hidden Markov model (MG-HMM) and fixed size least squares support vector regression (FS-LSSVR) for fault prognostic in equipment health management system.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Shop-Floor Production Performance Analysis Method for the Internet of Manufacturing Things

Zhang

Wang

Liu

et al. 2014

Advances in Mechanical Engineering

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Typical challenges that manufacturing enterprises are facing now are compounded by lack of timely, accurate, and consistent information of manufacturing resources. As a result, it is difficult to analyze the real-time production performance for the shopfloor. In this paper, the definition and overall architecture of the internet of manufacturing things is presented to provide a new paradigm by extending the techniques of internet of things (IoT) to manufacturing field. Under this architecture, the real-time primitive events which occurred at different manufacturing things such as operators, machines, pallets, key materials, and so forth can be easily sensed. Based on these distributed primitive events, a critical event model is established to automatically analyze the real-time production performance. Here, the up-level production performance analysis is regarded as a series of critical events, and the real-time value of each critical event can be easily calculated according to the logical and sequence relationships among these multilevel events. Finally, a case study is used to illustrate how to apply the designed methods to analyze the real-time production performance.

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“…The author in Ref. [12] provided a Lightweight Stage-based Event-Driven Process based on a layered architectural design, which is a novel CEP engine conceived with each of use. This system is mainly used for sense and responds application.…”

Section: Introductionmentioning

confidence: 99%

Online Monitoring of Manufacturing Process Based on autoCEP

Lei

Li²,

Kun³

2017

Int. J. Onl. Eng.

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Abstract-Complex Event Processing (CEP), which can identify patterns of interest from a large number of continuous data steam, is becoming more and more popular in manufacturing process monitoring. CEP rules are specified manually by domain expert, which is a limiting factor for its application in manufacturing enterprises. How to analysis historical data and automatically generate CEP rules is becoming a challenge research. This paper proposed a model of autoCEP for online monitoring in product manufacturing, which can automatically generate CEP rules based on association rules mining in key processes. First, the key quality factors in manufacturing process were extracted by grey entropy correlation analysis. Then, association rules mining method based on product process constraints was used to find the association rules between key factors and product quality. At last, the extracted rules are algorithmically transformed into CEP rules. The experimental results show the effectiveness and practicability of the proposed method.

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A lightweight and extensible Complex Event Processing system for sense and respond applications

Cited by 31 publications

References 29 publications

Appliance Recognition in an OSGi-Based Home Energy Management Gateway

Appliance Recognition in an OSGi-Based Home Energy Management Gateway

Real-Time Shop-Floor Production Performance Analysis Method for the Internet of Manufacturing Things

Online Monitoring of Manufacturing Process Based on autoCEP

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