DBEDU Series For Database Education

An integrated practicing platform enhancing the learning experiences in database related courses

NEURON

Learn query execution plans generated by RDBMS based on natural language descriptions.

DBinsight

Visually reveal details from SQL statements to execution plans.

LANTERN

Boredom-conscious natural language description generation of query execution plans.

MOCHA

A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education.

ARENA

Learn database optimization by comparing query execution plan and alternative plans.

RETRO

A visual tool for understanding cardinality estimation errors for database education.

ChatQPT

a novel system that enables chat-based interactions with a relational query engine (PostgreSQL).

# 📘NEURON | 📇DBinsight | 📰LANTERN | 💬MOCHA | ⚓️ARENA | 📊RETRO | 🤖ChatQPT | 🤖ChatQPT preview

ABOUT US

DBEDU presented by Prof. Hui Li (opens new window) AND His Group in Xidian University

Our Collaborators in DBEDU: Prof. Sourav S Bhowmick (opens new window) (NTU)

Supported DBMSs

This platform is currently compatible with the following DBMSs (may vary across subsystems):

The support for the followings are coming soon:

Team members

Current members: Dr. Xiyue Gao, Zihao Ma(Ph.D student), Zhenning Shi(Ph.D student) and 6 undergraduate students

Alumni: Weiguo Wang (Alibaba), Peng Chen (Huawei), Hu Wang (China CITIC Bank), Ying Rong (Huawei), Baochao Xu (NARI-Relays)

Publications

Liu et al. NEURON: Query Execution Plan Meets Natural Language Processing For Augmenting DB Education. In ACM SIGMOD, 2019. PDF (opens new window) video (opens new window)
Wang et al. Towards Enhancing Database Education: Natural Language Generation Meets Query Execution Plans. In ACM SIGMOD, 2021. PDF (opens new window) video (opens new window)
Chen et al. LANTERN: Boredom-conscious Natural Language Description Generation of Query Execution Plans for Database Education. In ACM SIGMOD, 2022. PDF (opens new window) video (opens new window)
Jess et al. MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education. In VLDB, 2022. PDF (opens new window) video (opens new window)
Rong et al. DBinsight: A Tool for Interactively Understanding the Query Processing Pipeline in RDBMSs. In CIKM, 2022. PDF (opens new window) video (opens new window)
Sourav S Bhowmick and Hui Li. Towards Technology-Enabled Learning of Relational Query Processing. In IEEE Data Eng. Bull. 45(3), 2022. PDF (opens new window)
Wang et al. ARENA: Alternative Relational Query Plan Exploration for Database Education. In ACM SIGMOD, 2023. PDF (opens new window)video (opens new window)
Hui Li. Technology-Enabled Database Education: Challenges and Opportunities. SIGMOD Record, 53(2). 2024. PDF (opens new window)
Sourav S Bhowmick and Hui Li. Experience Report on Using LANTERN in Teaching Relational Query Processing. In ACM SIGCSE 2025. PDF (opens new window)
Li et al. Towards Selecting the Informative Alternative Relational Query Plans for Database Education. Proc. ACM Manag. Data 4(3), SIGMOD, 2026. PDF (opens new window)
Zhang et al. RETRO: A Visual Tool for Understanding Cardinality Estimation Errors For Database Education. In ACM SIGMOD Companion, 2026. PDF (opens new window) SIGMOD Best Demonstration
Li et al. ChatQPT: Towards Conversing with Relational Query Engines. Proc. VLDB Endow., 2026.