Missing Data Filling Algorithm for Big Data-Based Map-Reduce Technology

Missing Data Filling Algorithm for Big Data-Based Map-Reduce Technology

Bayesian networks

Inference engines

Data handling

Data mining

Classification (of information)

MapReduce

Probabilistic logics

Query processing

In big data, the large number of missing values is a serious problem to compute the correct decision. This problem seriously affects the quality of information query, distorts data mining and analysis, and misleads the decisions. Therefore, in order to solve the missing values in the real database, the authors have prepopulated the missing data and filled in the classification attributes based on the probabilistic reasoning. The reasoning process is completed in Bayesian network to realize the parallelization of big data processing. The proposed algorithm has been presented in the MapReduce framework. The experimental results show that the Bayesian network construction method and probabilistic inference are effective for the classification data processing and the parallelism of algorithm in Hadoop. Copyright 2022, IGI Global.

2

18

Li, Fugui

Sharma, Ashutosh

International Journal of e-Collaboration

2022

journalArticle

15483673

10.4018/IJeC.304036