DARE Lab

Selected Publications

1. EDBT

   QPSeeker: An Efficient Neural Planner Combining Both Data and Queries through Variational Inference

   Tsapelas, Christos, and Koutrika, Georgia

   In Proceedings 27th International Conference on Extending Database Technology, EDBT 2024, Paestum, Italy, March 25 - March 28

   Bib HTML

   @inproceedings{DBLP:conf/edbt/TsapelasK24,
     author = {Tsapelas, Christos and Koutrika, Georgia},
     editor = {Tanca, Letizia and Luo, Qiong and Polese, Giuseppe and Caruccio, Loredana and Oriol, Xavier and Firmani, Donatella},
     title = {QPSeeker: An Efficient Neural Planner Combining Both Data and Queries through Variational Inference},
     booktitle = {Proceedings 27th International Conference on Extending Database Technology,
                       {EDBT} 2024, Paestum, Italy, March 25 - March 28},
     pages = {307--319},
     publisher = {OpenProceedings.org},
     year = {2024},
     url = {https://doi.org/10.48786/edbt.2024.27},
     doi = {10.48786/EDBT.2024.27},
     timestamp = {Fri, 22 Mar 2024 11:12:28 +0100},
     biburl = {https://dblp.org/rec/conf/edbt/TsapelasK24.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     abbr = {EDBT},
     bibtex_show = {true},
     selected = {true},
     html = {https://openproceedings.org/2024/conf/edbt/paper-82.pdf}
   }

2. 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}
   }

3. UMAP

   Optimizing Neighborhoods for Fair Top-N Recommendation

   Eleftherakis, Stavroula, Koutrika, Georgia, and Amer-Yahia, Sihem

   In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

   Abs Bib HTML

   We address demographic bias in neighborhood-learning models for collaborative filtering recommendations. Despite their superior ranking performance, these methods can learn neighborhoods that inadvertently foster discriminatory patterns. Little work exists in this area, highlighting an important research gap. A notable yet solitary effort, Balanced Neighborhood Sparse LInear Method (BNSLIM) aims at balancing neighborhood influence across different demographic groups. Yet, BNSLIM is hampered by computational inefficiency, and its rigid balancing approach often impacts accuracy. In that vein, we introduce two novel algorithms. The first, an enhancement of BNSLIM, incorporates the Alternating Direction Method of Multipliers (ADMM) to optimize all similarities concurrently, greatly reducing training time. The second, Fairly Sparse Linear Regression (FSLR), induces controlled sparsity in neighborhoods to reveal correlations among different demographic groups, achieving comparable efficiency while being more accurate. Their performance is evaluated using standard exposure metrics alongside a new metric for user coverage disparities. Our experiments cover various applications, including a novel exploration of bias in course recommendations by teachers’ country development status. Our results show the effectiveness of our algorithms in imposing fairness compared to BNSLIM and other well-known fairness approaches.

   @inproceedings{10.1145/3627043.3659539,
     author = {Eleftherakis, Stavroula and Koutrika, Georgia and Amer-Yahia, Sihem},
     title = {Optimizing Neighborhoods for Fair Top-N Recommendation},
     year = {2024},
     isbn = {9798400704338},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3627043.3659539},
     doi = {10.1145/3627043.3659539},
     booktitle = {Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
     pages = {57–66},
     numpages = {10},
     keywords = {balanced neighborhoods, collaborative filtering, fairness, neighborhood learning, recommender systems, sparse linear models},
     location = {Cagliari, Italy},
     series = {UMAP '24},
     abbr = {UMAP},
     bibtex_show = {true},
     html = {https://doi.org/10.1145/3627043.3659539},
     selected = {true}
   }

4. ICDE

   Guided SQL-Based Data Exploration with User Feedback

   Mandamadiotis, Antonis, Koutrika, Georgia, and Amer-Yahia, Sihem

   In 2024 IEEE 40th International Conference on Data Engineering (ICDE) May

   Abs Bib HTML

   The exploration of large, real-world databases poses major challenges to users due to their volume and complexity. SQL is the preferred language for data exploration. However, the process of iteratively refining SQL queries is tedious and time consuming. We formulate the automation of personalized SQL-based data exploration as the problem of suggesting the most relevant query and accounting for user feedback at each step. We develop an end-to-end solution and a system to assist users in exploring different components of a complex database. We instantiate our solution using Multi-Armed Bandits, a category of algorithms that are suitable for interactive online learning by balancing exploration with exploitation. We design a lightweight algorithm to personalize stepwise SQL recommendations that efficiently discovers the current user preferences in coordination with that user’s feedback and what other users prefer. We run extensive experiments that demonstrate the utility of our approach for large-scale data exploration.

   @inproceedings{10598168,
     author = {Mandamadiotis, Antonis and Koutrika, Georgia and Amer-Yahia, Sihem},
     booktitle = {2024 IEEE 40th International Conference on Data Engineering (ICDE)},
     title = {Guided SQL-Based Data Exploration with User Feedback},
     year = {2024},
     volume = {},
     number = {},
     pages = {4884-4896},
     keywords = {Structured Query Language;Automation;Databases;Large language models;Refining;Data engineering;Real-time systems;active learning;machine learning;recommender systems;data exploration;SQL query recommendations},
     doi = {10.1109/ICDE60146.2024.00372},
     issn = {2375-026X},
     month = may,
     abbr = {ICDE},
     bibtex_show = {true},
     html = {https://doi.org/10.1109/ICDE60146.2024.00372},
     selected = {true}
   }