International journal of database theory and application最新文献_第8页

Research on Quality Cost Model (QCM) based on Quality Improvement Procedure (QIP) in an Auto-factory 基于质量改进程序的汽车企业质量成本模型研究

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/ijdta.2017.10.1.26

F. Zhou, Xu Wang, Shan Chen, Yandong He, Lina Zhou

{"title":"Research on Quality Cost Model (QCM) based on Quality Improvement Procedure (QIP) in an Auto-factory","authors":"F. Zhou, Xu Wang, Shan Chen, Yandong He, Lina Zhou","doi":"10.14257/ijdta.2017.10.1.26","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.26","url":null,"abstract":"With the implementation of continues quality improvement procedure (QIP) in Chinese self-brand automotive firms, the quality-related cost needs identified correspondingly. Quality cost math models could reveal the mathematical relationship between the quality level and quality cost which provides the possibility for research on the trade-off of these two conflicting objectives, as well as contribute to quality related cost reduction. The quality index during QIP is established via Pearson coefficient within warranty period, related quality cost is categorized to conformance and non-conformance cost on the basis of the PAF ingredient as well. Four traditional quality cost math models have been analyzed in this paper, and the regression analysis based on curve fitting process has been implemented for a self-brand automotive firm during its QIP. The results verify that the four quality cost models (QCM) show their excellent simulating performance, which can uncover the optimal quality level and target R/1000@3MIS guiding correct operations during its QIP. In addition, the most appropriate quality performance level index is aggregated and calculated by employing a subjective AHP method, which specifies the quality improvement target ad potential cost reduction value.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"249 1","pages":"285-298"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75052118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Construction of Bi-modal Database for Barrier-free Teaching System 面向无障碍教学系统的双模态数据库构建

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.01

Jiling Tang, P. Feng, Zhanlei Li

引用次数: 0

Research on Deployment Strategies of Combine Harvesters Based on Intelligent Big Data Platform 基于智能大数据平台的联合收割机部署策略研究

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/ijdta.2017.10.1.24

Fan Zhang, Yan Zhang, Haizhao Yuan, Chuanyu Sun, Yihang Li

{"title":"Research on Deployment Strategies of Combine Harvesters Based on Intelligent Big Data Platform","authors":"Fan Zhang, Yan Zhang, Haizhao Yuan, Chuanyu Sun, Yihang Li","doi":"10.14257/ijdta.2017.10.1.24","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.24","url":null,"abstract":"The current agricultural machinery platforms just provide operational information of farmland and machinery, but not effective decision-making service. The problems of low utilization rate of agricultural machinery and low operation profits emerge as a major issue in the cross-regional operation of combine harvesters. The intelligent big data platform of agricultural machinery, which is firstly introduced, is not only to build an information exchanging platform for farmers and machine hand, but more important to provide the decision-making service. And then the deployment problem of combine harvesters is analyzed and the deployment model is established in the paper. Optimization deployment algorithm with global searching strategies, which is proposed in this paper, makes comparison with deployment algorithm with heuristic searching strategies that has be proposed in the author's previous article at aspects of deployment profit, cost and distances. It is concluded that the two algorithms have different applicable conditions. The better solution with high efficiency and performance can be obtained by the algorithm proposed in this paper.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"47 1","pages":"259-270"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83248313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Attack Model on Differential Privacy Preserving Methods for Correlated Time Series 相关时间序列差分隐私保护方法的攻击模型

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.09

Xiong Wenjun, Xu Zhengquan, Hao Wang

{"title":"An Attack Model on Differential Privacy Preserving Methods for Correlated Time Series","authors":"Xiong Wenjun, Xu Zhengquan, Hao Wang","doi":"10.14257/IJDTA.2017.10.1.09","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.09","url":null,"abstract":"Differential privacy has played a significant role in privacy preserving, and it has performed well in independent series. However, in real-world applications, most data are released in the form of correlated time series. Although a few differential privacy methods have focused on correlated time series, they are not designed by protecting against a specific attack model. Due to this drawback, the effectiveness of these methods cannot be verified and the privacy level of them cannot be measured. To address the problem, this paper presents an attack model based on the principle of filtering in signal processing theory. Since the distribution of the noise designed by current methods is independent and different from that of the original correlated series, a filter is designed as a unified attack model to sanitize the independent noise from the perturbed time series. Furthermore, the designed attack model can realize the function of measuring the effective privacy level of these methods and comparing the performance of them. Experimental results show that the attack model leads to degradation in privacy levels and can work as a unified measurement.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"100 1","pages":"89-104"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88962807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Semi-supervised Text Classification Using SVM with Exponential Kernel 基于指数核的SVM半监督文本分类

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.08

Liyun Zhong

{"title":"Semi-supervised Text Classification Using SVM with Exponential Kernel","authors":"Liyun Zhong","doi":"10.14257/IJDTA.2017.10.1.08","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.08","url":null,"abstract":"Kernel-based learning methods (kernel methods for short) in general and support vector machine (SVM) in particular have been successfully applied to the task of text classification. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. A significant challenge in text classification is to reduce the need for labeled training data while maintaining an acceptable performance. This paper presents a semi-supervised technique using the exponential kernel for text classification. Specifically, the semantic similarities between terms are first determined with both labeled and unlabeled training data by means of a diffusion process on a graph defined by lexicon and co-occurrence information, and the exponential kernel is then constructed based on the learned semantic similarity. Finally, the SVM classifier trains a model for each class during the training phase and this model is then applied to all test examples in the test phase. The main feature of this approach is that it takes advantage of the exponential kernel to reveal the semantic similarities between terms in an unsupervised manner, which provides a kernel framework for semi-supervised learning. The proposed approach is demonstrated on several benchmark data sets for text classification and the experimental results show that it can significantly improve the classification performance.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"49 1","pages":"79-88"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80479512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Network Data Mining Application in Earnings Management of Private Holding Enterprise: An Empirical Analysis Based on Multiple Regression Model 网络数据挖掘在民营控股企业盈余管理中的应用:基于多元回归模型的实证分析

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.25

Hong-Tao Liu

引用次数: 0

A Numerical Method for Suffix Array Index Compression 后缀数组索引压缩的数值方法

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.19

Baomin Xu, Jie Huang, Yang Yang

引用次数: 0

Assessing and Mining of Phylogenetic Trees 系统发育树的评估与挖掘

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.07

Geetika Munjal, M. Hanmandlu, Sangeet Srivastva, D. Gaur

引用次数: 2

An Improved Sequential Pattern Algorithm Based on Data Mining 一种基于数据挖掘的改进序列模式算法

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/IJDTA.2017.10.1.03

Jin Zhao, Runtao Lv, Yu Li

{"title":"An Improved Sequential Pattern Algorithm Based on Data Mining","authors":"Jin Zhao, Runtao Lv, Yu Li","doi":"10.14257/IJDTA.2017.10.1.03","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.03","url":null,"abstract":"This paper mentions several interestingness measures as Lift, Conviction, Piatetsky-Shapiro, Cosine, Jaccard and so on, which have proposed for mining association rules and classification rules but they have not been applied to mine sequential rules in sequence databases except the traditional measures of rule such as the support and confidence. We also propose then an efficient algorithm to generate all relevant sequential rules with the above interestingness measures from the prefix-tree which stored the whole sequential pattern where each child node stores a sequential pattern and its corresponding support value. By traversing the prefix-tree, the algorithm can then easily identify the components of a rule, and can calculate the measured values of the rule. The experimental results show that sequential rule mining with interestingness measures using the proposed algorithm based on the prefix-tree was always much faster than that using the other existing algorithm as modified Full. Especially when mining in large sequence databases with the low minimum support values, the number of sequential patterns generated from sequence databases was large and the proposed algorithm outperformed much because the proposed algorithm only traverse the prefix-tree to immediately determine which sequences are the left- and right-hand sides of a rule as well as their support values to compute the interestingness measure values of the rule from the sequential pattern set. In addition, the experimental results also show that the time for mining sequential rules with the confidence measure was the smallest, because it did not need to revisit the prefix-tree to determine the support of Y (the antecedence of rules), while the other interestingness measures need to revisit the prefix-tree to determine the support values of the consequent of rules or both the antecedence and the consequent.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"18 1","pages":"23-36"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87893797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal and Spatial Association Rules Strong Mining Algorithm Based on Hierarchical Reasoning Parameters 基于层次推理参数的时空关联规则强挖掘算法

International journal of database theory and application Pub Date : 2017-01-31 DOI: 10.14257/ijdta.2017.10.1.06

Zhang Xuewu

引用次数: 0