Incremental Food Sample Detection Method Based on Spark Framework
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-53980-1_84
Reference11 articles.
1. Bloom, D.J., Baik, H., Lee, S.: Beverage complexity yields unpredicted power results for seven discrimination test methods. J. Food Sci. 84(2), 606–612 (2019)
2. Huang, Z., Chen, Y., Wan, L.: GeoSpark SQL: an effective framework enabling spatial queries on spark. ISPRS Int. J. Geo-Inf. 6(9), 285–298 (2017)
3. Shi, W., Zhu, Y., Tian, H.: An integrated data preprocessing framework based on apache spark for fault diagnosis of power grid equipment. J. Sig. Proces. Syst. 86(2–3), 221–236 (2017)
4. Castro, M.R.D., Tostes, C.D.S., Dávila, A.M.R.: SparkBLAST: scalable BLAST processing using in-memory operations. BMC Bioinform. 18(1), 318–331 (2017)
5. Al-Zobbi, M., Shahrestani, S., Ruan, C.: Experimenting sensitivity-based anonymization framework in apache spark. J. Big Data 5(1), 38–45 (2018)
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