Abstract
To avoid the constraints of limited class hours and multiple teaching contents in the teaching process, this article designs an incremental update method for the data of the teaching resource database of economics majors. Firstly, use a combination of triggers and log tables to capture newly added teaching resource data. Then, the incremental data is sorted by clustering algorithm and loaded into the update log table of the In-memory database engine. Finally, data synchronization updates are completed through an event driven mechanism. The experimental results show that the incremental update efficiency obtained by this method is relatively higher, indicating that this method can better complete the incremental processing work. As the amount of data to be updated increases, this method consumes less time to complete the update, indicating a higher update efficiency.
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References
Liang, X., Yin, J.: Recommendation algorithm for equilibrium of teaching resources in physical education network based on trust relationship. J. Internet Technol. 23(1), 133–141 (2022)
Zhu, Y.Q., Cai, Y.M., Zhang, F.: Motion capture data denoising based on LSTNet autoencoder. J. Internet Technol. 23(1), 11–20 (2022)
Vlahek, D., Stoi, T., Golob, T., et al.: Method for estimating tensiomyography parameters from motion capture data. Informatica 45(2), 213 (2021)
Barakaz, F.E., Boutkhoum, O., Moutaouakkil, A.E.: A hybrid nave Bayes based on similarity measure to optimize the mixed-data classification. TELKOMNIKA (Telecommun. Comput. Electron. Control) 19(1), 155–162 (2021)
Irfan, M., Zheng, J., Iqbal, M., et al.: Brain inspired lifelong learning model based on neural based learning classifier system for underwater data classification. Exp. Syst. Appl. 186(1), 115798 (2021)
Brahmane, A.V., Krishna, C.B.: Rider chaotic biography optimization-driven deep stacked auto-encoder for big data classification using spark architecture: rider chaotic biography optimization. Int. J. Web Serv. Res. 18(3), 42–62 (2021)
Mohammed, M.S., Rachapudy, P.S., Kasa, M.: Big data classification with optimization driven MapReduce framework. Int. J. Knowl. Based Intell. Eng. Syst. 25(2), 173–183 (2021)
Wen, L., et al.: Accelerating molecular design of cage energetic materials with zero oxygen balance through large-scale database search. J. Phys. Chem. Lett. 12(47), 11591–11597 (2021). https://doi.org/10.1021/acs.jpclett.1c03728
Andreadi, N., Zankov, D., Karpov, K., et al.: Tree Parzen estimator for global geometry optimization: a benchmark and database of experimental gas-phase structures of organic molecules. J. Comput. Chem.Comput. Chem. 43(21), 1434–1441 (2022)
Yuan, S., Liu, Z., Tong, T.: (04021398) bond behaviors between UHPC and normal-strength concrete: experimental investigation and database construction. J. Mater. Civ. Eng. 34(1), 1–16 (2022)
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Li, T., Yang, H. (2024). The Incremental Updating Method of Economics Teaching Resource Database Data. In: Gui, G., Li, Y., Lin, Y. (eds) e-Learning, e-Education, and Online Training. eLEOT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-51471-5_10
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DOI: https://doi.org/10.1007/978-3-031-51471-5_10
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