Building complex event processing capability for intelligent environmental monitoring

Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams,...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 116; no. C; pp. 1 - 6
Main Authors: Sun, Alexander Y., Zhong, Zhi, Jeong, Hoonyoung, Yang, Qian
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.06.2019
Elsevier Science Ltd
Elsevier
Subjects:
Analytics
Anomalies
Anomaly detection
Carbon sequestration
Complex event processing
data analysis
Data management
Environmental monitoring
Geological carbon sequestration
Intelligent monitoring
Internet of Things
Machine learning
Monitoring systems
Real time
Smart sensors
systems engineering
Intelligent monitoring
Anomaly detection
Complex event processing
Machine learning
Geological carbon sequestration
ISSN:1364-8152, 1873-6726
Online Access:Get full text
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Summary:Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams, and the best practices for datastream processing and predictive analytics are yet to be established. This work presents a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. We show that the service-based CEP engine is instrumental for enabling environmental intelligent monitoring systems to ingest heterogeneous datastreams with scalable performance. Our CEP framework requires minimal coding from the user and can be easily extended to other similar environmental monitoring applications. •Wide use of smart sensors leads to explosive growth of data quantity and types.•We present an intelligent monitoring system enabled by complex event processing (CEP).•Machine learning algorithms are used to perform CEP.•Our system is built on an open-source stack of high-performance scalable components.•We demonstrate the system for a use case in geological carbon storage monitoring.
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USDOE
FE0026515
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2019.02.015