This paper analyzes the challenges, current limitations, and future prospects of database systems in modern information systems. Quantitative assessments indicate that NoSQL solutions can increase transaction processing speed by up to 40% compared to traditional SQL systems under high load conditions, while cloud-based architectures reduce maintenance costs by approximately 30% and improve system availability.
Summary. Automation of database indexing is a crucial component of modern database management systems that enhances search performance, scalability, and relevance in large-scale data environments. This paper explores the application of deep learning algorithms for building and optimizing vector indexes capable of automatic adaptation to changes in data structure and query patterns. An experimental comparison was conducted between traditional indexing methods (B-Tree, GIN in PostgreSQL) and vector-based indexing using Sentence-BERT embeddings implemented in FAISS and Milvus systems.
The article researches the principles of creation and assignment of a unique identifier inspireId to objects of classes in the topographic database (TDB), which is developed on the basis of the concept of model-driven architecture. The issue of automatic generation of a unique identifier inspireId and the rules of its life-cycle is researched.
Розроблено методи генерування крос-платформних відносно СКБД SQL-сценаріїв за допомогою XML-технологій.