<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid development of the data and information age, the digital-driven library has become an inevitable trend of library development. In this paper, through the Apriori association rule algorithm, the digital-driven library model for the influence factors of information mining, combined with data mining influence factor information to build a data-driven library management system, through the data library lending system optimization and book scheduling optimization to optimize and improve the overall optimization. Finally, it is verified through empirical analysis of association rule analysis and the scheduling effects of data-driven libraries. Through empirical analysis, it can be seen that the staff management efficiency of the management system lending system, and library circulation scheduling enhancement degree are 5.103 and 6.103, both greater than 1. The length of the reader queue and the percentage of reader loss of the data-driven library are between 5~15 and 0%~17%, respectively, and the efficiency of the data-driven library is superior to that of traditional libraries. Combined with the above, this paper based on the data-driven university library management service model can effectively solve the book scheduling problem, improve the management and service efficiency, so that readers have a better experience, and provide a guarantee for the innovative development path of the data-driven library management service model.</p>
</abstract>

With the rapid development of the data and information age, the digital-driven library has become an inevitable trend of library development. In this paper, through the Apriori association rule algorithm, the digital-driven library model for the influence factors of information mining, combined with data mining influence factor information to build a data-driven library management system, through the data library lending system optimization and book scheduling optimization to optimize and improve the overall optimization. Finally, it is verified through empirical analysis of association rule analysis and the scheduling effects of data-driven libraries. Through empirical analysis, it can be seen that the staff management efficiency of the management system lending system, and library circulation scheduling enhancement degree are 5.103 and 6.103, both greater than 1. The length of the reader queue and the percentage of reader loss of the data-driven library are between 5~15 and 0%~17%, respectively, and the efficiency of the data-driven library is superior to that of traditional libraries. Combined with the above, this paper based on the data-driven university library management service model can effectively solve the book scheduling problem, improve the management and service efficiency, so that readers have a better experience, and provide a guarantee for the innovative development path of the data-driven library management service model.

Data-driven innovation in university library management and service models

Suzhou University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Data-driven innovation in university library management and service models"}

With the rapid development of the data and information age, the digital-driven library has become an inevitable trend of library development. In this paper,...

Data-driven innovation in university library management and service models

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 25 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0229

Parole chiave
Apriori association rule algorithm, Information mining, Scheduling optimization, Data-driven, Library management

© 2024 Yan Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Data-driven innovation in university library management and service models

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 25 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0229

Parole chiaveApriori association rule algorithm, Information mining, Scheduling optimization, Data-driven, Library management

© 2024 Yan Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Apriori association rule algorithm, Information mining, Scheduling optimization, Data-driven, Library management