A Local Approach and Comparison with Other Data Mining Approaches in Software Application

2017 ◽  
pp. 1-26
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Forecast Accuracy ◽  
Software System ◽  
Local Approach ◽  
Test Analysis ◽  
Software Application ◽  
Software Aging ◽  
Optimum Approach ◽  
Sensitivity Specificity

In order to complete an online, real-time and effective aging detection to software, this paper studies a local approach that is also called a fuzzy incomplete and a statistical data mining approaches, and gives their algorithm implementation in the software system fault diagnosis. The application comparison of the two data mining approaches with four classical data mining approaches in software system fault diagnosis is discussed. The performance of each approach is evaluated from the sensitivity, specificity, accuracy rate, error classified rate, missed classified rate, and run-time. An optimum approach is chosen from several approaches to do comparative study. On the data of 1020 samples, the operating results show that the fuzzy incomplete approach has the highest sensitivity, the forecast accuracy that are 96.13% and 94.71%, respectively, which is higher than those of other approaches. It has also the relatively less error classified rate is or so 4.12%, the least missed classified rate is or so 1.18%, and the least runtime is 0.35s, which all are less than those of the other approaches. After the performance, indices are all evaluated and synthesized, the results indicate the performance of the fuzzy incomplete approach is best. Moreover, from the test analysis known, the fuzzy incomplete approach has also some advantages, such as it has the faster detection speed, the lower storage capacity, and does not need any prior information in addition to data processing. These results indicate that the mining approach is more effective and feasible than the old data mining approaches in software aging detection.

Predicting Software Abnormal State by using Classification Algorithm

2021 ◽  
pp. 1095-1113
Author(s):  
Yongquan Yan ◽  
Ping Guo
Keyword(s):  
Classification Algorithm ◽  
Software System ◽  
Selection Algorithm ◽  
Forward Selection ◽  
Software Application ◽  
Software Aging ◽  
Crash Failures ◽  
System Variables ◽  
A Minor ◽  
Fold Cross Validation

Software aging, also called smooth degradation or chronics, has been observed in a long running software application, accompanied by performance degradation, hang/crash failures or both. The key for software aging problem is how to fast and accurately detect software aging occurrence, which is a hard work due to the long delay before aging appearance. In this paper, two problems about software aging prediction are solved, which are how to accurately find proper running software system variables to represent system state and how to predict software aging state in a running software system with a minor error rate. Firstly, the authors use proposed stepwise forward selection algorithm and stepwise backward selection algorithm to find a proper subset of variables set. Secondly, a classification algorithm is used to model software aging process. Lastly, t-test with k-fold cross validation is used to compare performance of two classification algorithms. In the experiments, the authors find that their proposed method is an efficient way to forecast software aging problems in advance.

Predicting Software Abnormal State by using Classification Algorithm

2016 ◽  
Vol 27 (2) ◽  
pp. 49-65 ◽  
Author(s):  
Yongquan Yan ◽  
Ping Guo
Keyword(s):  
Classification Algorithm ◽  
Software System ◽  
Selection Algorithm ◽  
Forward Selection ◽  
Software Application ◽  
Software Aging ◽  
Crash Failures ◽  
System Variables ◽  
A Minor ◽  
Fold Cross Validation

Software aging, also called smooth degradation or chronics, has been observed in a long running software application, accompanied by performance degradation, hang/crash failures or both. The key for software aging problem is how to fast and accurately detect software aging occurrence, which is a hard work due to the long delay before aging appearance. In this paper, two problems about software aging prediction are solved, which are how to accurately find proper running software system variables to represent system state and how to predict software aging state in a running software system with a minor error rate. Firstly, the authors use proposed stepwise forward selection algorithm and stepwise backward selection algorithm to find a proper subset of variables set. Secondly, a classification algorithm is used to model software aging process. Lastly, t-test with k-fold cross validation is used to compare performance of two classification algorithms. In the experiments, the authors find that their proposed method is an efficient way to forecast software aging problems in advance.

An intelligent data mining system for drop test analysis of electronic products

2001 ◽  
Vol 24 (3) ◽  
pp. 222-231 ◽  
Author(s):  
Chi Zhou ◽  
P.C. Nelson ◽  
Weimin Xiao ◽  
T.M. Tirpak ◽  
S.A. Lane
Keyword(s):  
Data Mining ◽  
Drop Test ◽  
Mining System ◽  
Test Analysis ◽  
Electronic Products ◽  
Data Mining System

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

A New Design Method for the Fault Diagnosis on the Rotating Rectifier of Aeronautic Synchronous Generator

2012 ◽  
Vol 548 ◽  
pp. 544-547
Author(s):  
Yong Zhi Liu ◽  
Cong Liu
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Design Method ◽  
Synchronous Generator ◽  
New Method ◽  
Test Analysis ◽  
Exciting Current ◽  
On Line

A new method of fault diagnosis on the rotating rectifier of aeronautic synchronous is raised in the work. Firstly, the condition, truth and approach of EMD are introduced, and the method and steps of building up the feature vector are also included, Secondly the theories of LS-SVM and the arithmetic in the classification are also included. Finally taking the faults of one and two diodes turning off for example, after extracting the feature vector of exciting current based on EMD and establishing the classifying method based on Gauss RBF LS-SVM, the test, analysis and comparison can be on between LS-SVM and NN the conclusion can be got that the classified method referred in the work owns higher exactness, takes less time and has more application on the on-line fault diagnosis NN.

scholarly journals SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING

2011 ◽  
Vol 5 (3) ◽  
pp. 52
Author(s):  
Atje Setiawan ◽  
Rudi Rosadi
Keyword(s):  
Data Mining ◽  
Elementary School ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Education Quality ◽  
Percentage Error ◽  
Kriging Method ◽  
National Education ◽  
Software Application ◽  
Average Percentage

The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province.  The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords—  Expansion SAR, SAR-Kriging, quality education

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