MA'LUMOTLAR BAZASINI BOSHQARISHDA SUN'IY INTELLEKT: AVTOMATLASHTIRISH VA OPTIMALLASHTIRISH
Keywords:
Kalit so’zlar: sun’iy intellekt, ma’lumotlar bazasi, optimallashtirish, avtomatlashtirish, SQL so’rovlar, mashinali o’rganish, MBBT, intellektual monitoring.Abstract
Annotatsiya: Ushbu maqolada zamonaviy ma’lumotlar bazasini boshqarish
tizimlarining (MBBT) samaradorligini oshirishda sun’iy intellekt texnologiyalaridan
foydalanish masalalari tadqiq etiladi. Tadqiqot davomida katta hajmdagi ma’lumotlar
bilan ishlashda yuzaga keladigan murakkabliklarni kamaytirish, xususan, SQL
so’rovlarini optimallashtirish va tizim resurslarini avtomatik taqsimlashda mashinali
o’rganish algoritmlarining o’rni tahlil qilingan. Maqolada intellektual MBBTlarning
an’anaviy tizimlardan afzalliklari ko’rsatib o’tilgan hamda administratorlik vazifalarini
avtomatlashtirish orqali inson omilini kamaytirish bo’yicha xulosalar berilgan.
References
Foydalanilgan adabiyotlar
1. Kraska T., Alizadeh A., et al. "The Case for Learned Index Structures". SIGMOD
'18: Proceedings of the 2018 International Conference on Management of Data,
2018.
2. Pavlo A., et al. "Self-Driving Database Management Systems". CIDR 2017, 8th
Biennial Conference on Innovative Data Systems Research, 2017.
3. Zhang J., et al. "An End-to-End Automatic Cloud Database Tuning System Using
Deep Reinforcement Learning". SIGMOD '19, 2019.
4. Marcus R., et al. "Neo: A Learned Query Optimizer". Proceedings of the VLDB
Endowment, Vol. 12, No. 11, 2019.
5. Van Aken D., et al. "Automatic Database Management System Tuning Through
Large-scale Machine Learning". SIGMOD '17, 2017.
6. Oracle Corporation. "The Autonomous Database: A White Paper". Oracle Cloud
Infrastructure, 2023.
