Towards a Powerful Solution for Data Accuracy Assessment in the Big Data Context

Abstract

Data Accuracy is one of the main dimensions of Data Quality; it measures the degree to which data are correct. Knowing the accuracy of an organization's data reflects the level of reliability it can assign to them in decision-making processes. Measuring data accuracy in Big Data environment is a process that involves comparing data to assess with some "reference data" considered by the system to be correct. However, such a process can be complex or even impossible in the absence of appropriate reference data. In this paper, we focus on this problem and propose an approach to obtain the reference data thanks to the emergence of Big Data technologies. Our approach is based on the upstream selection of a set of criteria that we define as "Accuracy Criteria". We use furthermore a set of techniques such as Big Data Sampling, Schema Matching, Record Linkage, and Similarity Measurement. The proposed model and experiment results allow us to be more confident in the importance of data quality assessment solution and the configuration of the accuracy criteria to automate the selection of reference data in a Data Lake.

Analysis of the construction and reform path of preschool education professional practice courses in the context of computer big data

“…1, then the judgment result is consistent.The resulting pairwise comparison matrix iY is converted into a rough group decision matrix Y  , and the coarse number and coarse boundary spacing of this matrix are measured. The expressions are as follows:…”

“…In today's information age, with the continuous advancement of education informatization, the deep integration of big Internet data and information technology with higher vocational education is the general trend [1]. Teachers must effectively integrate various new technologies into educational teaching activities, and the combination of big Internet data and education and the collision of information technology and education has expanded the research field of teachers' professional development and promoted the continuous development of preschool teachers' specialization [2].…”

The Identification of Criteria for a Lab Report Scoring Checklist to Assess Practical Skills Indirectly in an Undergraduate Optics Course

“…Therefore, the 'correct uncertainties' criterion is included in the checklist. According to Talha et al (2020), data accuracy is important in the experiment because inaccurate data may lead to faulty conclusions. Therefore, the researcher thinks the 'accurate data' criterion is necessary to be included in the checklist.…”