VLDB 2026 Research / reviewers in the wild / expert
Mike Xydas
dblp:340/8438
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0002-8057-7023ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
2 papers |
Data models and query languages · 100% | |
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages › natural language interface
natural language interface to database |
1.3 | 2 | 2023 | Natural Language Interfaces for Databases with Deep Learning · Proc. VLDB Endow. 2023 Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface · WSDM 2023 |
Data models and query languages › natural language interface › natural language interface to database
text-to-SQL |
1.3 | 2 | 2023 | Natural Language Interfaces for Databases with Deep Learning · Proc. VLDB Endow. 2023 Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface · WSDM 2023 |
Natural language and speech › Question answering and dialogue systems
dialogue generation |
0.2 | 1 | 2023 | Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface · WSDM 2023 |
Methods — techniques the papers use, named apart from their topics
deep learning · 2.0large language model · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data InterfaceabstractIn the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system. George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika |
WSDM | 2 |
| 2023 | Natural Language Interfaces for Databases with Deep LearningabstractIn the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system. George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika |
Proc. VLDB Endow. | 2 |