An opinion based cross-regional meteorological event detection model

Weather ◽  
2018 ◽  
Vol 74 (2) ◽  
pp. 51-55 ◽  
Author(s):  
Yifan Zhu ◽  
James Chambua ◽  
Hao Lu ◽  
Kaize Shi ◽  
Zhendong Niu
2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096133
Author(s):  
Jianhua Wang ◽  
Bang Ji ◽  
Feng Lin ◽  
Shilei Lu ◽  
Yubin Lan ◽  
...  

Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.


2010 ◽  
Vol 19 (4) ◽  
pp. 817-828 ◽  
Author(s):  
Kuo ZHANG ◽  
Juan-Zi LI ◽  
Gang WU ◽  
Ke-Hong WANG

Event detection has wide application especially in the area of news streams analyzing where there is a need to monitor what events are emerging and affecting people’s lives. This is crucial for public administrations and policy makers to learn from their previous mistakes to make better decisions in the future. Different researchers have introduced several event detection models for Facebook news posts in. However, majority of these models have not provided adequate information about the discovered news events such as location, people and activity. In addition, existing models have ignored the problem of high dimensional feature space which affects the overall detection performance of the models. This research presents a conceptual event detection model for mining events from large volume of short text Facebook news posts and summarize their valuable information. This is crucial for public administrations and policy makers to learn from their previous mistakes to make better decisions in the future. The proposed model includes pre-processing, feature selection, event detection and summarization phases. The pre-processing phase involves several steps to convert unstructured text news posts into structure data. Feature selection phase to select the optimal feature subset. Meanwhile, event detection phase uses these features to construct undirected weighted graph and apply dynamic graph technique to identify the clusters from the graph and then annotate each cluster to its corresponding event. At the end of this paper, several unresolved problems in the construction of event detection model from Facebook news posts are reported to be used as future work for the current study.


Author(s):  
Jianhua Wang ◽  
Jun liu ◽  
Tao Wang ◽  
Lianglun Cheng

With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival, an efficient complex event detection model based on Extended Nondeterministic Finite Automaton (ENFA) is proposed in this paper. The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream. Specially, in our model, we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model, which can effectively address the problems above. The experimental results show that the proposed model in this paper outperforms some general models in saving detection time, memory consumption, detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.


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