References: [1] Dean, J., Ghemawat, S. (2004). MapReduce: Simplified Data Processing on Large Clusters. In: OSDI. [2] Duan, S., Thummala, V., Babu, S. (2009). Tuning Database Configuration Parameters with iTuned. ReCALL 2(1), 1246–1257 (aug). [3] Filho, E. R. L., de Almeida, E.C., Scherzinger, S. (2019). Don’t Tune Twice: Reusing Tuning Setups for SQL-on-Hadoop Queries. In: ER 2019 – 38th International Conference on Conceptual Modeling. [4] Filho, E. R. L., Picoli, I. L., de Almeida, E.C., Le Traon, Y., Chameleon. (2014). The Performance Tuning Tool for MapReduce Query Processing Systems. In: 29th SBBD – Demos and Applications Session – ISSN 2316-5170 October 6-9, 2014 – Curitiba, PR, Brazil. [5] Floratou, A., Minhas, U. F., Ozcan, F. (2014). SQL-on-Hadoop: full circle back to shared-nothing database architectures. Proceedings of the VLDB Endowment, 7(12), 1295–1306. [6] Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B., Babu, S. Starfish: A Self-Tuning System for Big Data Analytics. In: CIDR. [7] Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Zhang, N., Antony, S., Liu, H., Murthy, R. (2010). Hive - A petabyte scale data warehouse using hadoop. In: Proceedings - International Conference on Data Engineering. p 996–1005. [8] Yanpei Chen, S. A., Katz, R. H., Chen, Y., Alspaugh, S., Katz, R. (2012). Interactive Query Processing in Big Data Systems: A Cross Industry Study of MapReduce Workloads. Tech. Rep. 12, University of California, Berkeley. |