scholarly journals Model Driven Development Applied to Complex Event Processing for Near Real-Time Open Data

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4125 ◽  
Author(s):  
Pedro Clemente ◽  
Adolfo Lozano-Tello
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Open Data ◽  
Complex Event Processing ◽  
Data Sources ◽  
Event Processing ◽  
Model Driven Development ◽  
Model Driven ◽  
Concrete Syntax ◽  
Use Of The Internet

Nowadays, data are being produced like never before because the use of the Internet of Things, social networks, and communication in general are increasing exponentially. Many of these data, especially those from public administrations, are freely offered using the open data concept where data are published to improve their reutilisation and transparency. Initially, the data involved information that is not updated continuously such as budgets, tourist information, office information, pharmacy information, etc. This kind of information does not change during large periods of time, such as days, weeks or months. However, when open data are produced near to real-time such as air quality sensors or people counters, suitable methodologies and tools are lacking to identify, consume, and analyse them. This work presents a methodology to tackle the analysis of open data sources using Model-Driven Development (MDD) and Complex Event Processing (CEP), which help users to raise the abstraction level utilised to manage and analyse open data sources. That means that users can manage heterogeneous and complex technology by using domain concepts defined by a model that could be used to generate specific code. Thus, this methodology is supported by a domain-specific language (DSL) called OpenData2CEP, which includes a metamodel, a graphical concrete syntax, and a model-to-text transformation to specific platforms, such as complex event processing engines. Finally, the methodology and the DSL have been applied to two near real-time contexts: the analysis of air quality for citizens’ proposals and the analysis of earthquake data.

scholarly journals Complex Event Processing for Sensor Stream Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3084 ◽  
Author(s):  
Kyoungsoo Bok ◽  
Daeyun Kim ◽  
Jaesoo Yoo
Keyword(s):  
Real Time ◽  
Complex Event Processing ◽  
Superior Performance ◽  
Event Processing ◽  
Stream Data ◽  
Internet Of Things Environment ◽  
Processing Cost ◽  
Single Operator ◽  
Long Time ◽  
The Cost

As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods.

Visualisation and Detection of Road Traffic Events Using Complex Event Processing

2018 ◽  
Author(s):  
Paulo Figueiras ◽  
Hugo Antunes ◽  
Guilherme Guerreiro ◽  
Ruben Costa ◽  
Ricardo Jardim-Gonçalves
Keyword(s):  
Real Time ◽  
Road Network ◽  
Road Traffic ◽  
Complex Event Processing ◽  
Advanced Technology ◽  
Transport Systems ◽  
Automatic Identification ◽  
Event Processing ◽  
Time Data ◽  
The Road

In the recent decades, we have witnessed an increase in the number of vehicles using the road infrastructure, resulting in an increased overload of the road network. To mitigate such problems, caused by the increasing number of vehicles and increasing the efficiency and safety of transport systems has been integrated applications of advanced technology, denominated Intelligent Transport Systems (ITS). However, one problem still unsolved in current road networks is the automatic identification of road events such as accidents or traffic jams, being inhibitor to efficient road management. In order to mitigate this problematic, this paper proposes the development of a technological platform able to detect anomalies (abnormal traffic events) to typical road network status and categorize such anomalies. The proposed work, adopts a complex event processing (CEP) engine able to monitor streams of events and detect specified patterns of events in real time. Data is collectively collected and analysed in real-time from loop sensors deployed in Slovenian highways and national roads, providing traffic flows. This prototype will work with a large number of data, being used to process all data, complex event processing tools. All the data used to validate the present study is based on the Slovenian road network. This work has been carried out in the context of the OPTIMUM Project, funded by the H2020 European Research Framework Program.

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