DARE Lab

Selected Publications

1. SIGMOD

   Comparison and Analysis of Value Linking in Text-to-SQL Systems

   Koukouvinis, Apostolos, Katsogiannis-Meimarakis, George, and Koutrika, Georgia

   In SIGMOD

   Abs Bib PDF

   Value linking in text-to-SQL systems is the problem of associating values mentioned or implied within a NL query with their database entries. Despite existing efforts, their performance and comparative effectiveness remain unexplored. This work provides the first systematic study focused exclusively on value linking. First, we present a taxonomy that encapsulates the key steps and design choices for value linking. To enable a thorough analysis, we introduce VLD-Bench, a new, challenging benchmark that incorporates 18 categories of lexical and semantic discrepancies between values referenced in the queries and database entries. Our evaluation shows that state-of-the-art text-to-SQL systems suffer up to a 64% drop in execution accuracy due to value linking errors. A fine-grained analysis reveals how design choices impact robustness to linguistic variation and system latency, offering key insights and directions for future research.

   @inproceedings{koukouvinis2026valuelink,
     title = {Comparison and Analysis of Value Linking in Text-to-SQL Systems},
     author = {Koukouvinis, Apostolos and Katsogiannis-Meimarakis, George and Koutrika, Georgia},
     booktitle = {SIGMOD},
     year = {2026},
     abbr = {SIGMOD},
     bibtex_show = {true},
     selected = {true},
     pdf = {value_linking.pdf}
   }

2. 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}
   }

3. 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}
   }

4. 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}
   }

5. EDBT

   What Drives Learned Optimizer Performance? A Systematic Evaluation

   Mparmparousis, Kostas, Tsapelas, Christos, and Koutrika, Georgia

   In Proceedings 29th International Conference on Extending Database Technology, EDBT 2026, Tampere, Finland, March 24-27, 2026

   Bib HTML

   @inproceedings{DBLP:conf/edbt/MparmparousisTK26,
     author = {Mparmparousis, Kostas and Tsapelas, Christos and Koutrika, Georgia},
     editor = {Lehner, Wolfgang and Braganholo, Vanessa and Stefanidis, Kostas and Zhang, Zheying and Krause, Alexander and Pimentel, Jo{\~{a}}o Felipe Nicolaci},
     title = {What Drives Learned Optimizer Performance? {A} Systematic Evaluation},
     booktitle = {Proceedings 29th International Conference on Extending Database Technology,
                      {EDBT} 2026, Tampere, Finland, March 24-27, 2026},
     pages = {605--618},
     publisher = {OpenProceedings.org},
     year = {2026},
     url = {https://doi.org/10.48786/edbt.2026.49},
     doi = {10.48786/EDBT.2026.49},
     timestamp = {Wed, 18 Mar 2026 17:16:20 +0100},
     biburl = {https://dblp.org/rec/conf/edbt/MparmparousisTK26.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true},
     html = {https://openproceedings.org/2026/conf/edbt/paper-221.pdf},
     selected = {true},
     abbr = {EDBT}
   }

6. ICDE

   Query-Driven Data Exploration with Heterogeneous Treatment Effects

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

   In 2026 IEEE 42nd International Conference on Data Engineering (ICDE) May

   Abs Bib HTML

   Understanding how changes in actions or policies impact different population segments (users, items, etc.) is central to data-driven decision-making, but it is time-consuming and labor-intensive. We introduce a novel framework that combines user-defined search areas with causal inference to automatically identify responsive subgroups, i.e., subgroups most likely to benefit from a specific action or intervention, by estimating the impact of interventions, while also promoting subgroup diversity to ensure broad and representative insights. To support this, we develop a formal problem definition and an efficient search strategy to navigate the large combinatorial space of possible subgroups. We propose efficient algorithms that use pretrained causal effect estimators to dynamically surface the top- K subgroups. Experiments across multiple datasets and estimation techniques demonstrate the scalability and effectiveness of our framework for large-scale analysis.

   @inproceedings{11629194,
     author = {Mandamadiotis, Antonis and Amer-Yahia, Sihem and Koutrika, Georgia},
     booktitle = {2026 IEEE 42nd International Conference on Data Engineering (ICDE)},
     title = {Query-Driven Data Exploration with Heterogeneous Treatment Effects},
     year = {2026},
     volume = {},
     number = {},
     pages = {753-765},
     keywords = {Trees (botanical);Vegetation;Algorithms;Modeling;Printing;Forests;Timing;Training;Equations;Estimation;subgroup identification;causal inference;recommendations},
     doi = {10.1109/ICDE65706.2026.00062},
     issn = {2375-026X},
     month = may,
     bibtex_show = {true},
     abbr = {ICDE},
     html = {https://ieeexplore.ieee.org/document/11629194},
     selected = {true}
   }