Page 374 - 《软件学报》2026年第2期
P. 374

姬涛 等: AI 赋能的关系型数据库系统研究: 标准化、技术与挑战                                                853


                 [121]   Bruno N, Chaudhuri S. Automatic physical database tuning: A relaxation-based approach. In: Proc. of the 2005 ACM SIGMOD Int’l
                      Conf. on Management of Data. Baltimore: ACM, 2005. 227–238. [doi: 10.1145/1066157.1066184]
                 [122]   Ding  BL,  Das  S,  Marcus  R,  Wu  WT,  Chaudhuri  S,  Narasayya  VR.  AI  meets  AI:  Leveraging  query  executions  to  improve  index
                      recommendations.  In:  Proc.  of  the  2019  Int’l  Conf.  on  Management  of  Data.  Amsterdam:  ACM,  2019.  1241–1258.  [doi: 10.1145/
                      3299869.3324957]
                 [123]   Hammer M, Chan A. Index selection in a self-adaptive data base management system. In: Proc. of the 1976 ACM SIGMOD Int’l Conf.
                      on Management of Data. Washington: ACM, 1976. 1–8. [doi: 10.1145/509383.509385]
                 [124]   Liu XZ, Yin Z, Zhao C, Ge CC, Chen L, Gao YJ, Li DM, Wang ZT, Liang GZ, Tan J, Li FF. PinSQL: Pinpoint root cause SQLs to
                      resolve performance issues in cloud databases. In: Proc. of the 38th IEEE Int’l Conf. on Data Engineering. Kuala Lumpur: IEEE, 2022.
                      2549–2561. [doi: 10.1109/ICDE53745.2022.00236]
                 [125]   Laptev N, Amizadeh S, Flint I. Generic and scalable framework for automated time-series anomaly detection. In: Proc. of the 21st ACM
                      SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Sydney: ACM, 2015. 1939–1947. [doi: 10.1145/2783258.2788611]
                 [126]   Siffer A, Fouque PA, Termier A, Largouët C. Anomaly detection in streams with extreme value theory. In: Proc. of the 23rd ACM
                      SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Halifax: ACM, 2017. 1067–1075. [doi: 10.1145/3097983.3098144]
                 [127]   Ma MH, Yin Z, Zhang SL, Wang S, Zheng C, Jiang XH, Hu HW, Luo C, Li YL, Qiu NJ, Li FF, Chen CC, Pei D. Diagnosing root
                      causes  of  intermittent  slow  queries  in  cloud  databases.  Proc.  of  the  VLDB  Endowment,  2020,  13(8):  1176–1189.  [doi:  10.14778/
                      3389133.3389136]
                 [128]   Liu P, Zhang SL, Sun YQ, Meng Y, Yang JH, Pei D. FluxInfer: Automatic diagnosis of performance anomaly for online database
                      system. In: Proc. of the 39th IEEE Int’l Performance Computing and Communications Conf. Austin: IEEE, 2020. 1–8. [doi: 10.1109/
                      IPCCC50635.2020.9391550]
                 [129]   Yoon DY, Niu N, Mozafari B. DBSherlock: A performance diagnostic tool for transactional databases. In: Proc. of the 2016 Int’l Conf.
                      on Management of Data. San Francisco: ACM, 2016. 1599–1614. [doi: 10.1145/2882903.2915218]
                 [130]   Jeyakumar V, Madani O, Parandeh A, Kulshreshtha A, Zeng WF, Yadav N. ExplainIt!—A declarative root-cause analysis engine for
                      time series data. In: Proc. of the 2019 Int’l Conf. on Management of Data. Amsterdam: ACM, 2019. 333–348. [doi: 10.1145/3299869.
                      3314048]
                 [131]   Jin LY, Li GL. AI-based database performance diagnosis. Ruan Jian Xue Bao/Journal of Software, 2021, 32(3): 845–858 (in Chinese
                      with English abstract). http://www.jos.org.cn/1000-9825/6177.htm [doi: 10.13328/j.cnki.jos.006177]
                 [132]   Dintyala P, Narechania A, Arulraj J. SQLCheck: Automated detection and diagnosis of SQL anti-patterns. In: Proc. of the 2020 ACM
                      SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 2331–2345. [doi: 10.1145/3318464.3389754]
                 [133]   Sharma T, Fragkoulis M, Rizou S, Bruntink M, Spinellis D. Smelly relations: Measuring and understanding database schema quality. In:
                      Proc. of the 40th IEEE/ACM Int’l Conf. on Software Engineering: Software Engineering in Practice Track. Gothenburg: IEEE, 2018.
                      55–64.
                 [134]   Mao  YT,  Yuan  S,  Cui  N,  Du  TJ,  Shen  BJ,  Chen  YT.  DeFiHap:  Detecting  and  fixing  HiveQL  anti-patterns.  Proc.  of  the  VLDB
                      Endowment, 2021, 14(12): 2671–2674. [doi: 10.14778/3476311.3476316]
                 [135]   Khoussainova N, Balazinska M, Suciu D. PerfXplain: Debugging mapreduce job performance. Proc. of the VLDB Endowment, 2012,
                      5(7): 598–609. [doi: 10.14778/2180912.2180913]
                 [136]   Das S, Li F, Narasayya VR, König AC. Automated demand-driven resource scaling in relational database-as-a-service. In: Proc. of the
                      2016 Int’l Conf. on Management of Data. San Francisco: ACM, 2016. 1923–1934. [doi: 10.1145/2882903.2903733]
                 [137]   Higginson  AS,  Dediu  M,  Arsene  O,  Paton  NW,  Embury  SM.  Database  workload  capacity  planning  using  time  series  analysis  and
                      machine learning. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 769–783. [doi: 10.
                      1145/3318464.3386140]
                 [138]   Poppe O, Amuneke T, Banda D, et al. Seagull: An infrastructure for load prediction and optimized resource allocation. Proc. of the
                      VLDB Endowment, 2020, 14(2): 154–162. [doi: 10.14778/3425879.3425886]
                 [139]   Pimpley A, Li S, Sen R, Srinivasan S, Jindal A. Towards optimal resource allocation for big data analytics. In: Proc. of the 25th Int’l
                      Conf. on Extending Database Technology. Edinburgh: OpenProceedings.org, 2022. 2: 338–2: 350.
                 [140]   König AC, Shan Y, Ziegler T, Kakaraparthy A, Lang W, Moeller J, Kalhan A, Narasayya V. Tenant placement in over-subscribed
                      database-as-a-service clusters. Proc. of the VLDB Endowment, 2022, 15(11): 2559–2571. [doi: 10.14778/3551793.3551814]
                 [141]   Sun LM, Gong SJ, Zhang TY, Jiang FX, Zhao ZB, Chen JJ, Zhang XY. SUFS: A generic storage usage forecasting service through
                      adaptive ensemble learning. In: Proc. of the 39th IEEE Int’l Conf. on Data Engineering. Anaheim: IEEE, 2023. 3168–3181. [doi: 10.
                      1109/ICDE55515.2023.00243]
   369   370   371   372   373   374   375   376   377   378   379