Large-Scale Graph Neural Networks
With the recent rise in machine learning, neural network is also applied to graph data, which has a more versatile data structure, has gained significant attention. This project is working on developing technologies to process graph deep learning with higher accuracy and speed, as well as creating benchmarks to evaluate the performance of various graph neural networks.
Smart Agent-Based Modeling
Smart agents are intelligent, adaptive, and computational entities. While humans are the canonical smart agents, the advent of foundation models - imbued with remarkable language, vision, and reasoning abilities that emulate human behavior - enables us to expand the concept of smart agents to agent-based modeling (ABM). This evolution leads to the introduction of smart agent-based modeling (SABM). Unlike traditional ABM, SABM incorporates foundation models as agents and formulates models using natural language. We employ SABM to investigate natural processes across various fields such as economics and behavioral science. We believe that SABM offers a more nuanced and realistic approach to enhancing our comprehension of natural systems.
Optimizations for Large Language Models
Large language models (LLMs) are designed to handle and produce extensive natural language content. They develop an understanding of the structure, meaning, and knowledge embedded in human language datasets. Our focus includes three specific areas: (1) Fundamental technologies in Transformer-based LLMs, (2) Tailoring LLMs to specialized tasks, and (3) Refining methods for LLM agents.
Verifiable Data Ecosystem
We research and develop a verifiable data ecosystem that supports the reliability and lineage of data as first-class data, enabling the validation of any data. We establish a theoretical foundation for a comprehensive reliability and validation model, as well as for database repair considering transaction histories. Furthermore, we investigate the system architecture, including highly reliable distributed storage, and conduct empirical experiments with prototype systems, and verify the effectiveness of the proposed systems.
Machine Learning-Driven Query Optimization
The complexity of modern databases, combined with the ever-growing demand for faster data processing, poses significant challenges to query optimization. Despite advancements in database systems, achieving optimal performance for all queries remains difficult due to the vast number of execution plans and underlying data distributions. This project explores the integration of machine learning techniques into query optimization to automatically improve the execution of queries based on data patterns and workload characteristics.
Graph Database Management Systems
Graph data can model various relationships between objects and has practical applications in everyday life.
For instance, we can use knowledge graphs that structure relationships between objects for web search engines and recommendation systems.
Additionally, molecular data can be represented by atoms as objects and the bonds between atoms as relationships, enabling searches for molecules with the same data structure.
Social networking services and road networks are also closely related to our daily lives. The scale and diversity of graph data are continuously expanding.
There is a growing demand for database technologies that enable efficient management and fast searching of large-scale and diverse graph data.
In industries, graph database systems are among the most highly anticipated database systems, with major companies such as Amazon, Microsoft, and Google developing them.
However, there is still significant room for further development.
Our research focuses on fundamental technologies for graph database management and aims to develop a new database management system.
For example, we explore indexing and query optimization techniques to accelerate graph data queries, as well as the use of machine learning to speed up searches.
In addition, we contribute graph database scandalizations.