PUEGM: A Method of User Revenue Selection Based on a Publisher-User Evolutionary Game Model for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a low cost. Low-quality data will reduce the efficiency of MCS and lead to a loss of revenue. However, existing work lacks research on the selection of user revenue under the premise of ensuring data quality. In this paper, we propose a Publisher-User Evolutionary Game Model (PUEGM) and a revenue selection method to solve the evolutionary stable equilibrium problem based on non-cooperative evolutionary game theory. Firstly, the choice of user revenue is modeled as a Publisher-User Evolutionary Game Model. Secondly, based on the error-elimination decision theory, we combine a data quality assessment algorithm in the PUEGM, which aims to remove low-quality data and improve the overall quality of user data. Finally, the optimal user revenue strategy under different conditions is obtained from the evolutionary stability strategy (ESS) solution and stability analysis. In order to verify the efficiency of the proposed solutions, extensive experiments using some real data sets are conducted. The experimental results demonstrate that our proposed method has high accuracy of data quality assessment and a reasonable selection of user revenue.

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The Research of Data Points Quality Assessment Method in Reverse Engineering

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.419-420.445 ◽

2009 ◽

Vol 419-420 ◽

pp. 445-448 ◽

Cited By ~ 2

Author(s):

Jun Ting Cheng ◽

Wei Ling Zhao ◽

Can Zhao ◽

Xue Dong Xie

Keyword(s):

Reverse Engineering ◽

Data Quality ◽

Quality Assessment ◽

Assessment Method ◽

Measured Data ◽

Quality Data ◽

Data Quality Assessment ◽

Quality Assessment Method ◽

Data Points ◽

Data Point

In the field of reverse engineering, data quality assessment is a very important work in the detection, the result of data quality assessment will directly or indirectly affect the detection and the following manufacturing process quality. Data quality assessment can be used in the camera calibration, the model and model reconstruction comparison, and so on. In this paper, on the basis of the existing method of calculating each point error, and multipurpose use of average and standard error and some other concepts of mathematical statistics, and then improve a novel and simple calculating error method. This method is applicable to many groups of one-to-one ideal data and the measured data comparison, and it can be more intuitive to reflect the error of overall data, as well as the error distribution, and it can be more efficient to determine the measured data is reasonable or not. In this paper, the data point quality which is collected in the reverse engineering is assessed, and it can see that the method which is proposed in this article has some advantages in the data point quality assessment field.

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A Review of Data Quality Assessment in Emergency Medical Services

The Open Medical Informatics Journal ◽

10.2174/1874431101812010019 ◽

2018 ◽

Vol 12 (1) ◽

pp. 19-32 ◽

Cited By ~ 1

Author(s):

Mehrnaz Mashoufi ◽

Haleh Ayatollahi ◽

Davoud Khorasani-Zavareh

Keyword(s):

Data Quality ◽

Quality Assessment ◽

Emergency Medical Services ◽

Emergency Care ◽

Quality Data ◽

Medical Services ◽

Care Services ◽

Emergency Medical ◽

Data Quality Assessment ◽

Emergency Care Services

Introduction:Data quality is an important issue in emergency medicine. The unique characteristics of emergency care services, such as high turn-over and the speed of work may increase the possibility of making errors in the related settings. Therefore, regular data quality assessment is necessary to avoid the consequences of low quality data. This study aimed to identify the main dimensions of data quality which had been assessed, the assessment approaches, and generally, the status of data quality in the emergency medical services.Methods:The review was conducted in 2016. Related articles were identified by searching databases, including Scopus, Science Direct, PubMed and Web of Science. All of the review and research papers related to data quality assessment in the emergency care services and published between 2000 and 2015 (n=34) were included in the study.Results:The findings showed that the five dimensions of data quality; namely, data completeness, accuracy, consistency, accessibility, and timeliness had been investigated in the field of emergency medical services. Regarding the assessment methods, quantitative research methods were used more than the qualitative or the mixed methods. Overall, the results of these studies showed that data completeness and data accuracy requires more attention to be improved.Conclusion:In the future studies, choosing a clear and a consistent definition of data quality is required. Moreover, the use of qualitative research methods or the mixed methods is suggested, as data users’ perspectives can provide a broader picture of the reasons for poor quality data.

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Data Quality Assessment

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch041 ◽

2009 ◽

pp. 378-384 ◽

Cited By ~ 2

Author(s):

Juliusz L. Kulikowski

Keyword(s):

Information Processing ◽

Data Quality ◽

Quality Assessment ◽

Quality Data ◽

Quality Of Data ◽

High Quality ◽

Data Quality Assessment ◽

Systems Effectiveness ◽

And Control ◽

Education Science

For many years the fact that for a high information processing systems’ effectiveness high quality of data is not less important than high systems’ technological performance was not widely understood and accepted. The way to understanding the complexity of data quality notion was also long, as it will be shown below. However, a progress in modern information processing systems development is not possible without improvement of data quality assessment and control methods. Data quality is closely connected both with data form and value of information carried by the data. High-quality data can be understood as data having an appropriate form and containing valuable information. Therefore, at least two aspects of data are reflected in this notion: 1st - technical facility of data processing, and 2nd - usefulness of information supplied by the data in education, science, decision making, etc.

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Data Quality Assessment

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch020 ◽

2005 ◽

pp. 116-120 ◽

Cited By ~ 4

Author(s):

Juliusz L. Kulikowski

Keyword(s):

Information Processing ◽

Data Quality ◽

Quality Assessment ◽

Quality Data ◽

Quality Of Data ◽

High Quality ◽

Data Quality Assessment ◽

Systems Effectiveness ◽

And Control ◽

Education Science

For many years the idea that for high information processing systems effectiveness, high quality of data is not less important than the systems’ technological perfection was not widely understood and accepted. The way to understanding the complexity of the data quality notion was also long, as will be shown in this paper. However, progress in modern information processing systems development is not possible without improvement of data quality assessment and control methods. Data quality is closely connected both with data form and value of information carried by the data. High-quality data can be understood as data having an appropriate form and containing valuable information. Therefore, at least two aspects of data are reflected in this notion: (1) technical facility of data processing and (2) usefulness of information supplied by the data in education, science, decision making, etc.

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