Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Research on Library Big Data Cleaning System based on Big Data Decision Analysis Needs

Authors
Jianfeng Liao, Jianping You, Qun Zhang
Corresponding Author
Jianfeng Liao
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.62How to use a DOI?
Keywords
University library; Big data decision analysis; Data cleaning and filtering; Data integration application.
Abstract
In the era of big data, university library information management services must be based on actual conditions, using high-quality data to improve big data management. However, high-quality big data is useful data that needs to be filtered and classified. Big data cleansing is an effective way to improve data quality. To this end, the paper proposes to integrate the data resources of efficient libraries, analyze the source and type of useless data, and design a hierarchical management model of data. The model includes management operation level, data cleaning and filtering level, data integration level and big data. At the resource utilization level, after attempting to filter invalid data through data cleaning, the complexity of big data decision analysis is reduced, library big data integration is promoted, big data decision-making is realized, and the possibility of library big data integration and sharing is improved.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.62How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jianfeng Liao
AU  - Jianping You
AU  - Qun Zhang
PY  - 2019/04
DA  - 2019/04
TI  - Research on Library Big Data Cleaning System based on Big Data Decision Analysis Needs
PB  - Atlantis Press
SP  - 377
EP  - 382
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.62
DO  - https://doi.org/10.2991/icmeit-19.2019.62
ID  - Liao2019/04
ER  -