Event-driven architecture for decision support in traffic management systems

Dunkel, Jürgen; Fernandez, Alberto; Ortiz, Rubén Fernández; Ossowskí, Sascha

doi:10.1016/j.eswa.2010.11.087

Cited by 76 publications

(23 citation statements)

References 18 publications

(22 reference statements)

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“…Objects in the Internet of Things will not only be devices with sensory capabilities, but also provide actuation capabilities (e.g., bulbs or locks controlled over the Internet) (Ersue et al 2014). Next are brief write-ups to serve as practical examples of ambient intelligence-based and sensors-based decision support systems that have been developed (Chernbumroong et al 2014;Droit et al 2008;Filip 2008;El-Hachem et al 2012;Dunkel et al 2011;Rodger 2014;Fricoteaux et al 2014;Edoura-Gaena et al 2006;Howells et al 1999;Paraskevopoulos and Singels 2014;Kaupp et al 2010;Qi et al 2014;Ansola et al 2012). …”

Section: Ambient Intelligence and The Internet Of A Things-based Decimentioning

confidence: 98%

“…Decision support systems for traffic management systems have to cope with a high volume of events continuously generated by sensors (Dunkel et al 2011). Dunkel et al (2011 propose a reference architecture for event-driven traffic management systems, which enables the analysis and processing of complex event streams in real-time and is therefore well-suited for decision support in sensorbased traffic control systems.…”

Section: Ambient Intelligence and The Internet Of A Things-based Decimentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Section: Ambient Intelligence and The Internet Of A Things-based Decimentioning

confidence: 98%

Section: Ambient Intelligence and The Internet Of A Things-based Decimentioning

confidence: 99%

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…It is important to highlight that the code generated by our editor is the same as the code shown in Dunkel et al (2011). Therefore, we can affirm that our editor can adequately model event patterns in different domains and can correctly generate the EPL code for these patterns.…”

Section: Editor and Epl Code Generation Evaluationmentioning

confidence: 88%

“…According to Luckham (2012, chap. 5), some of the major areas for sales of CEP are: fraud detection and security (Edge and Falcone Sampaio, 2012), transportation and traffic management (Dunkel et al, 2011), health care (Yuan and Lu, 2009;Yao et al, 2011), energy and manufacturing (Vikhorev et al, 2013), locationbased services (Uhm et al, 2011), financial systems and operations (Edge and Falcone Sampaio, 2012), and operational intelligence in business (Chaudhuri et al, 2011). Among other additional areas, CEP can also be applied to home automation (Romero et al, 2011) and RFID signals (Yao et al, 2011).…”

Section: Complex Event Processingmentioning

confidence: 99%

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A model-driven approach for facilitating user-friendly design of complex event patterns

Boubeta-Puig

Ortiz

Medina‐Bulo

2014

Expert Systems with Applications

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Complex Event Processing (CEP) is an emerging technology which allows us to efficiently process and correlate huge amounts of data in order to discover relevant or critical situations of interest (complex events) for a specific domain. This technology requires domain experts to define complex event patterns, where the conditions to be detected are specified by means of event processing languages. However, these experts face the handicap of defining such patterns with editors which are not user-friendly enough. To solve this problem, a model-driven approach for facilitating user-friendly design of complex event patterns is proposed and developed in this paper. Besides, the proposal has been applied to different domains and several event processing languages have been compared. As a result, we can affirm that the presented approach is independent both of the domain where CEP technology has to be applied to and of the concrete event processing language required for defining event patterns.

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Towards pattern‐based architectures for event processing systems

Bruns¹,

Dunkel²

2013

Softw Pract Exp

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SUMMARYRecently, event processing (EP) has gained considerable attention as an individual discipline in computer science. From a software engineering perspective, EP systems still lack the maturity of well‐established software architectures. For the development of industrial EP systems, generally accepted software architectures based on proven design patterns and principles are still missing. In this article, we introduce a catalog of design patterns that supports the development of event‐driven architectures (EDAs) and complex EP systems. The design principles originate from experiences reported in publications as well as from our own experiences in building EP systems with industrial and academic partners. We present several patterns on different layers of abstractions that define the overall structure as well as the building blocks for EP systems. Architectural patterns that determine the top‐level structure of an EDA can be distinguished from design patterns that specify the basic mechanisms of EP. The practical application of the catalog of patterns is described by the pattern‐based design of a sample EDA for a sensor‐based energy control system. Finally, we propose a coherent and general reference architecture for EP derived from the proposed patterns.Copyright © 2013 John Wiley & Sons, Ltd.

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Event-driven architecture for decision support in traffic management systems

Cited by 76 publications

References 18 publications

Intelligent Decision Support Systems

Intelligent Decision Support Systems

A model-driven approach for facilitating user-friendly design of complex event patterns

Towards pattern‐based architectures for event processing systems

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