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

Boubeta-Puig, Juan; Ortiz, Guadalupe; Medina‐Bulo, Inmaculada

doi:10.1016/j.eswa.2013.07.070

Cited by 31 publications

(21 citation statements)

References 11 publications

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“…Second, we would like to apply our combined fuzzy automata and CEP approach to other application domains where CEP has already been successfully used. For example, we are considering healthcare and network security and we will take as initial step our previous work (Boubeta-Puig, Ortiz, & Medina-Bulo, 2014, 2015Macià, Valero, Díaz, Boubeta-Puig, & Ortiz, 2016). computer science, assigned by the Italian Chapter of the European Association for Theoretical Computer Science.…”

Section: Discussionmentioning

confidence: 99%

Analysis of temporal complex events in sensor networks

Boubeta-Puig

Bravetti

Llana

et al. 2017

Journal of Information and Telecommunication

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In this paper we introduce a framework for detecting anomalies in the clocks of the different components of a network of sensor stations connected with a central server for measuring of air quality. Local clocks of sensor stations can be advanced/delayed with respect to the central server clock and this situation provokes the inaccuracy in the interpretation of the collected data. We propose a novel approach, supported by a formal representation of the network using fuzzy-timed automata, to precisely represent the expected behaviour of each component of the network. Using fuzzy logic concepts, we can specify admissible mismatches between the clocks. In addition, we apply complex event processing (CEP) technology in order to automatically detect situations of interest while processing the massive amount of data transferred across the network. Specifically, we have designed a collection of CEP patterns that trigger alarms when unexpected differences are observed. We also report the results obtained from the application of our approach to the network

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Section: Discussionmentioning

confidence: 99%

Analysis of temporal complex events in sensor networks

Boubeta-Puig

Bravetti

Llana

et al. 2017

Journal of Information and Telecommunication

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“…Appropriate CEP engines (also know as inference engines) already exist for complex event processing, such as Storm [38], Drools [39], Jess [40], and Esper [39], [41]. It play useful roles in formulating machine-readable rules for determining the trigger sequences of events for a particular activity or process.…”

Section: Complex Event Processing (Cep)mentioning

confidence: 99%

Automated Decision Support IoT Framework

Lunardi

Amaral

Marczak

et al. 2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

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During the past few years, with the fast development and proliferation of the Internet of Things (IoT), many application areas have started to exploit this new computing paradigm. The number of active computing devices has been growing at a rapid pace in IoT environments around the world. Consequently, a mechanism to deal with this different devices has become necessary. Middleware systems solutions for IoT have been developed in both research and industrial environments to supply this need. However, decision analytics remain a critical challenge. In this work we present the Decision Support IoT Framework composed of COBASEN, an IoT search engine to address the research challenge regarding the discovery and selection of IoT devices when large number of devices with overlapping and sometimes redundant functionality are available in IoT middleware systems, and DMS, a rule-based reasoner engine allowing to set up computational analytics on device data when it is still in motion, extracting valuable information from it for automated decision making. DMS uses Complex Event Processing to analyze and react over streaming data, allowing for example, to trigger an actuator when a specific error or condition appears in the stream. The main goal of this work is to highlight the importance of a decision support system for decision analytics in the IoT paradigm. We developed a system which implements DMS concepts. However, for preliminarily tests, we made a functional evaluation of both systems in terms of performance. Our initial findings suggest that the Decision Support IoT Framework provides important approaches that facilitate the development of IoT applications, and provides a new way to see how the business rules and decision-making will be made towards the Internet of Things.

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“…Differing from Storm, Drools [18] and Esper [10,19] are CEP engines which include a module providing native support for events evaluation and temporal logic analysis in a single work node. Yao et al [18] presented a Drools based CEP framework which was used to process surgical events and provided sense and response capability for hospitals.…”

Section: Related Workmentioning

confidence: 99%

“…According to CEP, different algorithms have been proposed to increase Complex Event Processing capability. They adopt CEP engine (like Storm [7], Drools [18], and Esper [10,19]) in their applications, while others design new event processing engines and system architectures [11,12,15,16,20] with evaluation to show the usefulness and scalability.…”

Section: Related Workmentioning

confidence: 99%

A Novel Complex Event Processing Engine for Intelligent Data Analysis in Integrated Information Systems

Wang

Zhou

Ali

et al. 2016

International Journal of Distributed Sensor Networks

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Novel and effective engines for data analysis in integrated information systems are urgently required by diverse applications, in which massive business data can be analyzed to enable the capturing of various situations in real time. The performance of existing engines has limited capacity of data processing in distributed computing. Although Complex Event Processing (CEP) has enhanced the capacity of data analysis in information systems, it is still a big challenging task since events are rapidly increasing in diverse applications. In this paper, a lightweight intelligent data analysis system with a novel CEP engine named LIDA-E is introduced, which employs the knowledge base with rules and an event processing algorithm for analysis. Event models as well as operators support the rules for event selection and aggregation. These operators and rules have been utilized for constructing new CEP system architecture which combines expressiveness and efficiency in analysis. It adopts the agents and filter conception explicitly to provide the event transmission mechanism efficiently. Finally, the comparison between the proposed engine and the existing engine shows that LIDA-E has 48.65% averagely reduced time cost in different tests. The experimental results demonstrate that the developed architecture has better performance in both transmitting and analyzing a large number of events.

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

Cited by 31 publications

References 11 publications

Analysis of temporal complex events in sensor networks

Analysis of temporal complex events in sensor networks

Automated Decision Support IoT Framework

A Novel Complex Event Processing Engine for Intelligent Data Analysis in Integrated Information Systems

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