Application Generation Framework for Real-Time Complex Event Processing

2008 ◽  
Author(s):  
Yonit Magid ◽  
Asaf Adi ◽  
Maya Barnea ◽  
David Botzer ◽  
Ella Rabinovich
Keyword(s):  
Real Time ◽  
Complex Event Processing ◽  
Event Processing

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.

scholarly journals Real-time complex event processing for cloud resources

2017 ◽  
Vol 898 ◽  
pp. 042020 ◽  
Author(s):  
M Adam ◽  
C Cordeiro ◽  
L Field ◽  
D Giordano ◽  
L Magnoni
Keyword(s):  
Real Time ◽  
Complex Event Processing ◽  
Event Processing ◽  
Cloud Resources

scholarly journals Wayeb: a Tool for Complex Event Forecasting

10.29007/2s9t ◽  
2018 ◽  
Author(s):  
Elias Alevizos ◽  
Alexander Artikis ◽  
Georgios Paliouras
Keyword(s):  
Markov Chains ◽  
Computational Model ◽  
Real Time ◽  
Complex Event Processing ◽  
Pattern Detection ◽  
Event Processing ◽  
Timely Manner ◽  
Probabilistic Description ◽  
Event Forecasting ◽  
Symbolic Automata

Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real–time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.

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