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[4] Smutny P, Schreiberova P. Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education,
2020, 151: 103862. [doi: 10.1016/j.compedu.2020.103862]
[5] Cao W, Gao YS, Li FF, Wang S, Lin BC, Xu K, Feng XJ, Wang YC, Liu ZJ, Zhang GJ. Timon: A timestamped event database for
efficient telemetry data processing and analytics. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland:
ACM, 2020. 739–753. [doi: 10.1145/3318464.3386136]
®
[6] Apache Flink . 2024. https://flink.apache.org/
[7] Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I. Spark: Cluster computing with working sets. In: Proc. of the 2nd USENIX
Conf. on Hot Topics in Cloud Computing. Boston: USENIX Association, 2010. 10.
[8] Fragkoulis M, Carbone P, Kalavri V, Katsifodimos A. A survey on the evolution of stream processing systems. The VLDB Journal, 2024,
33(2): 507–541. [doi: 10.1007/s00778-023-00819-8]
[9] Sun DW, Zhang GY, Zheng WM. Big data stream computing: Technologies and instances. Ruan Jian Xue Bao/Journal of Software, 2014,
25(4): 839–862 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4558.htm [doi: 10.13328/j.cnki.jos.004558]
[10] Xu ZZ, Xu C, Ding GY, Chen ZH, Zhou AY. Research progress on key technologies towards real-time stream processing applications.
Ruan Jian Xue Bao/Journal of Software, 2024, 35(1): 430–454 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6917.
htm [doi: 10.13328/j.cnki.jos.006917]
[11] Isah H, Abughofa T, Mahfuz S, Ajerla D, Zulkernine F, Khan S. A survey of distributed data stream processing frameworks. IEEE
Access, 2019, 7: 154300–154316. [doi: 10.1109/ACCESS.2019.2946884]
[12] Chintapalli S, Dagit D, Evans B, Farivar R, Graves T, Holderbaugh M, Liu Z, Nusbaum K, Patil K, Peng BJ, Poulosky P. Benchmarking
streaming computation engines: Storm, Flink and spark streaming. In: Proc. of the 2016 IEEE Int’l Parallel and Distributed Processing
Symp. Workshops (IPDPSW). Chicago: IEEE, 2016. 1789–1792. [doi: 10.1109/IPDPSW.2016.138]
[13] Terry D, Goldberg D, Nichols D, Oki B. Continuous queries over append-only databases. ACM SIGMOD Record, 1992, 21(2): 321–330.
[doi: 10.1145/141484.130333]
[14] Javed MH, Lu XY, Panda DK. Characterization of big data stream processing pipeline: A case study using Flink and Kafka. In: Proc. of
the 4th IEEE/ACM Int’l Conf. on Big Data Computing, Applications and Technologies. Austin: ACM, 2017. 1–10. [doi: 10.1145/
3148055.3148068]
[15] Chandrasekaran S, Cooper O, Deshpande A, Franklin MJ, Hellerstein JM, Hong W, Krishnamurthy S, Madden SR, Reiss F, Shah MA.
TelegraphCQ: Continuous dataflow processing. In: Proc. of the 2003 ACM SIGMOD Int’l Conf. on Management of Data. San Diego:
ACM, 2003. 668. [doi: 10.1145/872757.872857]
[16] Gedik B, Andrade H, Wu KL, Yu PS, Doo M. SPADE: The system S declarative stream processing engine. In: Proc. of the 2008 ACM
SIGMOD Int’l Conf. on Management of Data. Vancouver: ACM, 2008. 1123–1134. [doi: 10.1145/1376616.1376729]
[17] Arasu A, Babcock B, Babu S, Cieslewicz J, Datar M, Ito K, Motwani R, Srivastava U, Widom J. STREAM: The Stanford data stream
management system. In: Garofalakis M, Gehrke J, Rastogi R, eds. Data Stream Management. Berlin: Springer, 2016. 317–336. [doi: 10.
1007/978-3-540-28608-0_16]
[18] Abadi DJ, Carney D, Çetintemel U, Cherniack M, Convey C, Lee S, Stonebraker M, Tatbul N, Zdonik S. Aurora: A new model and
architecture for data stream management. The VLDB Journal, 2003, 12(2): 120–139. [doi: 10.1007/s00778-003-0095-z]
[19] Abadi DJ, Ahmad Y, Balazinska M, Çetintemel U, Cherniack M, Hwang JH, Lindner W, Maskey A, Rasin A, Ryvkina E, Tatbul N, Xing
Y, Zdonik SB. The design of the borealis stream processing engine. In: Proc. of the 2nd Biennial Conf. on Innovative Data Systems
Research. Asilomar, 2005. 277–289.
[20] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107–113.
[doi: 10.1145/1327452.1327492]
[21] Apache Storm. 2023. https://storm.apache.org/
[22] Armbrust M, Das T, Torres J, Yavuz B, Zhu SX, Xin R, Ghodsi A, Stoica I, Zaharia M. Structured streaming: A declarative API for real-
time applications in Apache Spark. In: Proc. of the 2018 Int’l Conf. on Management of Data. Houston: ACM, 2018. 601–613. [doi: 10.
1145/3183713.3190664]
[23] Carbone P, Ewen S, Fóra G, Haridi S, Richter S, Tzoumas K. State management in Apache Flink®: Consistent stateful distributed stream
processing. Proc. of the VLDB Endowment, 2017, 10(12): 1718–1729. [doi: 10.14778/3137765.3137777]
[24] Zeuch S, Monte BD, Karimov J, Lutz C, Renz M, Traub J, Breß S, Rabl T, Markl V. Analyzing efficient stream processing on modern
hardware. Proc. of the VLDB Endowment, 2019, 12(5): 516–530. [doi: 10.14778/3303753.3303758]
[25] Koliousis A, Weidlich M, Fernandez RC, Wolf AL, Costa P, Pietzuch P. SABER: Window-based hybrid stream processing for
heterogeneous architectures. In: Proc. of the 2016 Int’l Conf. on Management of Data. San Francisco: ACM, 2016. 555–569. [doi: 10.