Volume 39, Number 2, April 2021
|Page(s)||430 - 438|
|Published online||09 June 2021|
- Liu Wenjie, Li Jianbo, Li Zhanhuai, et al. A massire distribnted relational database for financial application[J]. Journal of Huazhong University, 2019, 47(2): 121–126 (in Chinese) [Google Scholar]
- Darko Makreshanski, Jana Giceva, Claude Barthels, et al. BatchDB: efficient isolated execution of hybrid OLTP+OLAP workloads for interactive applications[C]//Proceedings of the 2017 ACM International Conference on Management of Data, New York, 2017 [Google Scholar]
- Sadoghi M, Bhattacherjee S, Bhattacharjee B, et al. L-store: a real-time OLTP and OLAP system[C]//Proceedings of the 21st International Confcrence on Extendirg Database Technology, 2018 [Google Scholar]
- Zhang K, Sadoghi M, Jacobsen H. DL-store: a distributed hybrid OLTP and OLAP data processing engine[C]//IEEE 36th International Conference on Distributed Computing Systems, 2016: 769–770 [Google Scholar]
- Ronald Barber, Vijayshankar Raman, Richard Sidlc, et al. Wildfire: HTAP for big data[M]. Encyclopedia of Big Data Technologies, 2019 [Google Scholar]
- Boissier M, Schlosser R, Uflacker M. Hybrid data layouts for tiered HTAP databases with pareto-optimal data placements[C]//IEEE 34th International Conference on Data Engineering, 2018 [Google Scholar]
- Chaalal Hichem, Travers Nicolas, Belbachir Hafida. T-plotter: a new data structure to reconcile OLAP and OLTP Models[J]. Multiagent and Grid Systems, 2019, 1: 237[J]–257 [Article] [Google Scholar]
- Hedjazi M A, Kourbane I, Genc Y, et al. A comparison of hadoop, spark and storm for the task of large scale image classification[C]//2018 26th Signal Processing and Communications Applications Conference, Izmir, 2018 [Google Scholar]
- Markl V. Mosaics: stratosphere, flink and beyond[C]//IEEE International Conference on Data Engineering, 2017 [Google Scholar]
- Kamil Jerábek, Ondrej Rysary. Big data network flow processing using apache spark[C]//Proceedings of the 6th Conference on the Engineering of Computer Based Systems, 2019 [Google Scholar]
- Chintapalli S, Dagit D, Evans B, et al. Benchmarking streaming computation engines: storm, flink and spark streaming[C]//IEEE International Parallel & Distributed Processing Symposium Workshops, 2016 [Google Scholar]
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