Chengjing Xia
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Chengjing Xia has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chengyun Zhang
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
1 shared publication - Daoqu Geng
The Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, People’s Republic of China
1 shared publication - Qilin Liu
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
1 shared publication - Xinshuai Fu
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
1 shared publication - Xue Xia
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
1 shared publication
Latest Publications
- Open Access
- 3 April 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 44574-44582
Big Data-Based Improved Data Acquisition and Storage System for Designing Industrial Data Platform
Big data-based acquisition and storage system (ASS) plays an important role in the design of industrial data platform. Many big data frameworks have been integrated compression and serialization method. These methods cannot meet the needs of industrial production information management for requiring time-consuming and mass storage. Based on the existing big data frameworks, we propose an enhanced industrial big data platform in order to reduce the data processing time while requiring fewer data storage space. Specifically, this paper focuses on evaluating the impact of multiple compression and serialization methods on big data platform performance and tries to choose optimal compression and serialization methods for the industrial data platform. Compared to the methods integrated in Hadoop and Spark, the experimental results showed the data compression time of the platform has been reduced by 73.9% with a less than 96% the size of data compressed, furthermore, the data serialization time has been reduced by 80.8%. With the increasing amount of data, it takes less time to compare with benchmark methods.
- 3 April 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 44574-44582
Open AccessBig Data-Based Improved Data Acquisition and Storage System for Designing Industrial Data Platform
Big data-based acquisition and storage system (ASS) plays an important role in the design of industrial data platform. Many big data frameworks have been integrated compression and serialization method. These methods cannot meet the needs of industrial production information management for requiring time-consuming and mass storage. Based on the existing big data frameworks, we propose an enhanced industrial big data platform in order to reduce the data processing time while requiring fewer data storage space. Specifically, this paper focuses on evaluating the impact of multiple compression and serialization methods on big data platform performance and tries to choose optimal compression and serialization methods for the industrial data platform. Compared to the methods integrated in Hadoop and Spark, the experimental results showed the data compression time of the platform has been reduced by 73.9% with a less than 96% the size of data compressed, furthermore, the data serialization time has been reduced by 80.8%. With the increasing amount of data, it takes less time to compare with benchmark methods.