Graph database is a data management system centered on graph structures, using nodes, edges, and properties to represent and store data, with a strong emphasis on describing relationships between data entities.

Distributed database is a database system in which data logically belongs to a single database but is physically stored across multiple computing nodes.

LLMOps (Large Language Model Operations) refers to a comprehensive set of methodologies and operational practices that manage the entire lifecycle of large language models (LLMs), including data preparation, model training, deployment, monitoring, and continuous optimization.

As a new type of data storage and retrieval tool, vector databases have demonstrated their unique value across multiple fields.

Relational databases and vector databases are two distinct tools for storing and retrieving data. Each has unique advantages and is suited for different application scenarios.

Privacy computing enables data sharing and computation while safeguarding data privacy, providing robust support for application scenarios across multiple industries.

As data security and privacy protection gain increasing attention, privacy-preserving computation has become a critical enabler for promoting data circulation and utilization.

Privacy-preserving computation is not a single technology, but a sophisticated technical system that integrates a variety of underlying technologies including hardware, cryptography, and distributed machine learning.

Federated learning, also known as federated machine learning, collaborative learning or alliance learning, is built on the core idea of enabling multi-party collaborative machine learning model training through the circulation and processing of intermediate encrypted data, without directly sharing raw data.

Secure Multi-party Computation is a technology that enables multiple parties to jointly compute a target function without disclosing their respective input data.