Relyatsion modelni optimallashtirishda suniy intellektdan foydalanishning ahamiyati
Annotatsiya
Ushbu maqolada relyatsion ma'lumotlar bazalarini optimallashtirishda suniy intellekt texnologiyalaridan foydalanishning nazariy asoslari va amaliy natijalari ko'rib chiqiladi. So'rovlarni avtomatik optimallashtirish, indekslash strategiyalari, prediktor modellar va mashinali o'rganish algoritmlarini ma'lumotlar bazalari bilan integratsiya qilish masalalari tahlil etiladi.
Kalit so‘zlar: relyatsion model, suniy intellekt, SQL optimallashtirish, mashinali o'rganish, ma'lumotlar bazasi, indekslash, so'rov rejalashtirish.
Библиографические ссылки
1. Marcus, R., & Papaemmanouil, O. (2019). Plan-structured deep neural network models for query performance prediction. VLDB.
2. Marcus, R. et al. (2021). Bao: Making Learned Query Optimization Practical. ACM SIGMOD.
3. Kraska, T. et al. (2018). The Case for Learned Index Structures. ACM SIGMOD.
4. Pavlo, A. et al. (2017). Self-Driving Database Management Systems. CIDR Conference.
5. Oracle Corporation (2023). Oracle Autonomous Database: Technical Documentation.
6. Microsoft (2023). Intelligent Query Processing in SQL Server. Microsoft Docs.
