Novel method for the evaluation of data quality based on fuzzy control

Ban, Xiaojuan; Ning, Shurong; Zhao-lin, XU; Cheng, Peng

doi:10.1016/s1004-4132(08)60127-9

Cited by 10 publications

(4 citation statements)

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“…There are plenty of ways of date preprocessing. In this paper, we use three of them: data cleaning [11], data integration [12], and data reduction [13].…”

Section: B the Data Mining Based Users Clusterring Algorithmmentioning

confidence: 99%

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A Trust-based Information Propagation Model in Online Social Networks

Liu¹,

Sun²

2013

JNW

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As a new media, social network gains a wide range of social influence, and the social network based e-commerce becomes more and more popular, therefor, the study of information propagation is great of significance. In this paper, we will presents a trust based information propagation model, just as the trust between people in real life. First, employ the algorithm of data mining on personal information to make the classification of the user. And then calculate the value of trust between the users according their relationships. At last, this paper predicts the range of information propagation by using the trust value between the users and messages. The last part of the paper is the certification of the model by using an actual example.

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“…There are plenty of ways of date preprocessing. In this paper, we use three of them: data cleaning [11], data integration [12], and data reduction [13].…”

Section: B the Data Mining Based Users Clusterring Algorithmmentioning

confidence: 99%

“…where 1 ( , ) V C k is the user set in k C which has direct interact with user k, 1 ( , ) kC  is a parameter, which is calculated as 1 11 ( , )…”

Section: Trust Calculation Between the Clusters In The Online Social mentioning

confidence: 99%

A Trust-based Information Propagation Model in Online Social Networks

Liu¹,

Sun²

2013

JNW

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“…Data quality issues are becoming increasingly prominent. Therefore, how to improve the data quality has become an indispensable link [2][3].…”

Section: Introductionmentioning

confidence: 99%

Power System Data Quality Evaluation Based on the Analytic Hierarchy Process

Wang¹,

Xuan

Liu³

et al. 2013

AMR

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The demand for electric power data is more and more widely, and put forward higher requirements to the quality of statistical data. This paper combined with the features of electric power data. Evaluate data quality from the accuracy, completeness, uniqueness, consistency, accuracy, efficiency and timeliness seven aspects. And put forward the specific evaluation methods of each evaluation index. Then build a whole data quality evaluation process on this basis, quantitative analysis the data in the database, to acquaintance the data quality condition.

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“…This should have a high impact on the way applications are built, as developers are greatly interested in programming systems that are reliable, even in the presence of poor quality data. The problem is that although the analysis and improvement of data quality have gathered plenty of attention (e.g., to carry out data cleaning operations) from practitioners and researchers [6,[24][25][26][27][28], and despite the well-known impact of poor quality data in critical data-centric systems [29], understanding how well an application is prepared to handle the inevitable appearance of poor data has been largely overlooked. For this purpose, the identification of representative data quality problems and how they should be integrated in software verification activities (e.g., software testing) is essential.…”

Section: Background and Related Workmentioning

confidence: 99%

Testing data-centric services using poor quality data: from relational to NoSQL document databases

et al. 2017

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Businesses are nowadays deploying their services online, reaching out to clients all around the world. Many times deployed as web applications or web services, these business-critical systems typically perform large amounts of database operations; thus, they are dependent on the quality of the data to provide correct service to clients. Research and practice have shown that the quality of the data in an enterprise system gradually decreases overtime, bringing in diverse reliability issues to the applications that are using the data to provide services. These issues range from simple incorrect operations to aborted operations or severe system failures. In this paper, we present an approach to test data-centric services in presence of poor quality data. The approach has been designed to consider relational and NoSQL database nodes used by the system under test and is based on the injection of poor quality data on the database-application interface. The results indicate the effectiveness of the approach in discovering issues, not only at the application-level, but also in the middleware being used, contributing to the development of more reliable services.

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Novel method for the evaluation of data quality based on fuzzy control

Cited by 10 publications

References 0 publications

A Trust-based Information Propagation Model in Online Social Networks

A Trust-based Information Propagation Model in Online Social Networks

Power System Data Quality Evaluation Based on the Analytic Hierarchy Process

Testing data-centric services using poor quality data: from relational to NoSQL document databases

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