DARE Lab

Selected Publications

1. VLDBJ

   A survey on deep learning approaches for text-to-SQL

   Katsogiannis-Meimarakis, George, and Koutrika, Georgia

   The VLDB Journal

   Abs Bib HTML

   To bridge the gap between users and data, numerous text-to-SQL systems have been developed that allow users to pose natural language questions over relational databases. Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this survey is to present a detailed taxonomy of neural text-to-SQL systems that will enable a deeper study of all the parts of such a system. This taxonomy will allow us to make a better comparison between different approaches, as well as highlight specific challenges in each step of the process, thus enabling researchers to better strategise their quest towards the “holy grail” of database accessibility.

   @article{katsogiannis2023survey,
     author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia},
     title = {A survey on deep learning approaches for text-to-SQL},
     journal = {The VLDB Journal},
     year = {2023},
     issn = {0949-877X},
     doi = {10.1007/s00778-022-00776-8},
     url = {https://doi.org/10.1007/s00778-022-00776-8},
     html = {https://doi.org/10.1007/s00778-022-00776-8},
     abbr = {VLDBJ},
     bibtex_show = {true},
     selected = {true}
   }

2. VLDB

   DatAgent: the imminent age of intelligent data assistants

   Mandamadiotis, Antonis, Eleftherakis, Stavroula, Glenis, Apostolos, Skoutas, Dimitrios, Stavrakas, Yannis, and Koutrika, Georgia

   Proceedings of the VLDB Endowment

   Abs Bib HTML

   In this demonstration, we present DatAgent, an intelligent data assistant system that allows users to ask queries in natural language, and can respond in natural language as well. Moreover, the system actively guides the user using different types of recommendations and hints, and learns from user actions. We will demonstrate different exploration scenarios that show how the system and the user engage in a human-like interaction inspired by the interaction paradigm of chatbots and virtual assistants.

   @article{mandamadiotis2021datagent,
     title = {DatAgent: the imminent age of intelligent data assistants},
     author = {Mandamadiotis, Antonis and Eleftherakis, Stavroula and Glenis, Apostolos and Skoutas, Dimitrios and Stavrakas, Yannis and Koutrika, Georgia},
     journal = {Proceedings of the VLDB Endowment},
     volume = {14},
     number = {12},
     pages = {2815--2818},
     year = {2021},
     publisher = {VLDB Endowment},
     bibtex_show = {true},
     abbr = {VLDB},
     html = {http://vldb.org/pvldb/vol14/p2815-mandamadiotis.pdf},
     selected = {true}
   }