An efficient complex event detection model for high proportion disordered RFID event stream

2017 ◽  
Vol 08 (04) ◽  
pp. 1750049
Author(s):  
Jianhua Wang ◽  
Jun liu ◽  
Tao Wang ◽  
Lianglun Cheng
Keyword(s):  
Event Detection ◽  
Detection Time ◽  
Memory Consumption ◽  
Related Event ◽  
Event Stream ◽  
Detection Model ◽  
Detection Latency ◽  
Model Based ◽  
Proposed Model ◽  
Nondeterministic Finite Automaton

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.

An Efficient Complex Event Processing Algorithm Based on NFA-HTBTS for Massive RFID Event Stream

2018 ◽  
Vol 11 (2) ◽  
pp. 18-30
Author(s):  
Jianhua Wang ◽  
Shilei Lu ◽  
Yubin Lan ◽  
Lianglun Cheng
Keyword(s):  
Detection Efficiency ◽  
Hash Table ◽  
Complex Event Processing ◽  
Processing Method ◽  
Tree Structure ◽  
Event Processing ◽  
Memory Consumption ◽  
Event Stream ◽  
Simulation Results ◽  
Nondeterministic Finite Automaton

This article describes how quickly picking up some valuable information from massive RFID event stream often faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of volume, velocity, variety, value and veracity. Aim to solving the problems above, an efficient complex event processing method based on NFA-HTBTS (Nondeterministic Finite Automaton-Hash Table B+ Tree Structure) is presented in this article. The achievement of this article lies in that we successfully use the union of NFA and HTBTS to realize the detection of complex event in massive RFID event stream. Specially, in our scheme, after using NFA to match related primitive events from massive RFID event stream, we use hash table and B+ tree structure to successfully realize the detection of complex event from large matched results above, as a result, these problems existed in current methods above can be effectively solved by our scheme. The simulation results show that our proposed scheme outperforms some general methods for massive RFID event stream.

scholarly journals A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096133
Author(s):  
Jianhua Wang ◽  
Bang Ji ◽  
Feng Lin ◽  
Shilei Lu ◽  
Yubin Lan ◽  
...  
Keyword(s):  
Event Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Scheme ◽  
Event Stream ◽  
Detection Model ◽  
Efficient Detection ◽  
High Efficient ◽  
Complex Events

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.

scholarly journals A New Event Detection Model Based on Term Reweighting

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

EDM-JBW: A novel event detection model based on JS-ID′F and Bikmeans with word embedding for news streams

2018 ◽  
Vol 28 ◽  
pp. 336-342 ◽  
Author(s):  
Pengpeng Zhou ◽  
Zhen Cao ◽  
Bin Wu ◽  
Chunzi Wu ◽  
Shuqi Yu
Keyword(s):  
Event Detection ◽  
Word Embedding ◽  
Detection Model ◽  
Model Based

scholarly journals Off-topic English Essay Detection Model Based on Hybrid Semantic Space for Automated English Essay Scoring System

2018 ◽  
Vol 232 ◽  
pp. 01035
Author(s):  
Guimin Huang ◽  
Jian Liu ◽  
Chunli Fan ◽  
Tingting Pan
Keyword(s):  
Scoring System ◽  
Noun Phrases ◽  
Semantic Space ◽  
Data Sets ◽  
Topic Detection ◽  
Accuracy Rate ◽  
Detection Model ◽  
Model Based ◽  
Proposed Model ◽  
Dependency Parser

Aiming at the problem that the lack of accurate and efficient off-topic detection model for current Automated English Scoring System in China, an unsupervised off-topic essay detection model based on hybrid semantic space was proposed. Firstly, the essay and its essay prompt are respectively represented as noun phrases by using a neural-network dependency parser. Secondly, we introduce a method to construct a hybrid semantic space. Thirdly, we propose a method to represent the noun phrases of the essay and its prompt as vectors in hybrid semantic space and calculate the similarity between the essay and its prompt by using the noun phrase vectors of them. Finally, we propose a sort method to set the off-topic threshold so that the off-topic essays can be identified efficiently. The experimental results on four datasets totaling 5000 essays show that, compared to the previous off-topic essay detection models, the proposed model can detect off-topic essays with higher accuracy, and the accuracy rate over all essay data sets reaches 89.8%.

An Immune Intrusion Detection Model Based on One-Class Classification Technology

2012 ◽  
Vol 457-458 ◽  
pp. 979-984
Author(s):  
Yan Zhang ◽  
Cai Ming Liu ◽  
Run Chen ◽  
Hong Ying Qin ◽  
Bin Li
Keyword(s):  
Intrusion Detection ◽  
Feature Space ◽  
Support Vector ◽  
Network Attack ◽  
Detection Model ◽  
Model Based ◽  
Proposed Model ◽  
Domain Description ◽  
The One ◽  
One Class Classification

An intrusion detection model based on biological immune principle and one-class classification technology is proposed. The one-class classification technology named support vector domain description (SVDD) is applied to the proposed model. Simple multi-dimension feature vectors of network packets are mapped into high dimension feature space. The description models of the antibody and the self set are constructed. The evolution process of antibodies is described with math language. The theoretical analysis shows that the proposed model can detect network attack effectively, and unknown network attacks can be detected.

A Modified Dugoff Tire Model for Combined-slip Forces

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Vehicle Dynamics ◽  
Tire Model ◽  
Magic Formula ◽  
Model Based ◽  
Proposed Model ◽  
Road Friction ◽  
On Line ◽  
Double Lane Change ◽  
Tire Forces ◽  
Tire Models

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.

Sign in / Sign up

Export Citation Format

Share Document