Selected Recent Publications

2025

1. EDBT

   Analysis of Text-to-SQL Benchmarks: Limitations, Challenges and Opportunities

   Mitsopoulou, Anna, and Koutrika, Georgia

   In Proceedings 28th International Conference on Extending Database Technology, EDBT 2025, Barcelona, Spain, March 25-28, 2025

   Bib HTML

   @inproceedings{DBLP:conf/edbt/MitsopoulouK25,
     author = {Mitsopoulou, Anna and Koutrika, Georgia},
     editor = {Simitsis, Alkis and Kemme, Bettina and Queralt, Anna and Romero, Oscar and Jovanovic, Petar},
     title = {Analysis of Text-to-SQL Benchmarks: Limitations, Challenges and Opportunities},
     booktitle = {Proceedings 28th International Conference on Extending Database Technology,
                       {EDBT} 2025, Barcelona, Spain, March 25-28, 2025},
     pages = {199--212},
     publisher = {OpenProceedings.org},
     year = {2025},
     url = {https://doi.org/10.48786/edbt.2025.16},
     doi = {10.48786/EDBT.2025.16},
     timestamp = {Fri, 12 Jul 2024 15:24:41 +0200},
     biburl = {https://dblp.org/rec/conf/edbt/MitsopoulouK25.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     abbr = {EDBT},
     bibtex_show = {true},
     html = {https://openproceedings.org/2025/conf/edbt/paper-41.pdf}
   }

2024

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. VLDB

   ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems

   Zhang, Yi, Deriu, Jan, Katsogiannis-Meimarakis, George, Kosten, Catherine, Koutrika, Georgia, and Stockinger, Kurt

   Proceedings of the VLDB Endowment
3. ICDE

   FreqyWM: Frequency Watermarking for the New Data Economy

   İşler, Devriş, Cabana, Elisa, García-Recuero, Álvaro, Koutrika, Georgia, and Laoutaris, Nikolaos

   In Proceedings of the 40th IEEE International Conference on Data Engineering
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. 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}
   }

2023

1. VLDB

   Natural Language Interfaces for Databases with Deep Learning

   Katsogiannis-Meimarakis, George, Xydas, Mike, and Koutrika, Georgia

   Proc. VLDB Endow. Sep

   Abs HTML

   In 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.

2. BigVis

   Natural Language Data Interfaces: From Keyword Search to ChatGPT, Are We There Yet?

   Katsogiannis-Meimarakis, George, Tsapelas, Christos, and Koutrika, Georgia

   In Proceedings of the 6th International Workshop on Big Data Visual Exploration and Analytics co-located with EDBT/ICDT 2023 Joint Conference

   HTML
3. WSDM

   Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface

   Katsogiannis-Meimarakis, George, Xydas, Mike, and Koutrika, Georgia

   In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

   Abs HTML

   In 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.

4. 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}
   }

2022

1. TWC

   Data Democratisation with Deep Learning: An Analysis of Text-to-SQL Systems

   Katsogiannis-Meimarakis, George, and Koutrika, Georgia

   In Companion Proceedings of the Web Conference 2022

   Abs HTML

   In 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 increasing complexity of data makes data querying all the more challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable querying relational databases using natural language. The recent advances on deep neural networks along with the creation of two large datasets specifically made for training text-to-SQL systems, have paved the path for a novel and very promising research area. The purpose of this tutorial is a deep dive into this area, covering state-of-the-art techniques for natural language representation in neural networks, benchmarks that sparked research and competition, recent text-to-SQL systems using deep learning techniques, as well as open problems and research opportunities.

2021

1. SIGMOD

   A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems

   Katsogiannis-Meimarakis, George, and Koutrika, Georgia

   In Proceedings of the 2021 International Conference on Management of Data

   Abs Bib HTML

   Data is a prevalent part of every business and scientific domain,but its explosive volume and increasing complexity make data querying challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable querying relational databases using natural language. The recent advances on deep neural networks along with the creation of two large datasets specifically made for training text-to-SQL systems, have paved the path for a novel and very promising research area. The purpose of this tutorial is a deep dive into this area, covering state-of-the-art techniques for natural language representation in neural networks,benchmarks that sparked research and competition, recent text-to-SQL systems using deep learning techniques, as well as open problems and research opportunities.

   @inproceedings{10.1145/3448016.3457543,
     author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia},
     title = {A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems},
     year = {2021},
     isbn = {9781450383431},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3448016.3457543},
     doi = {10.1145/3448016.3457543},
     booktitle = {Proceedings of the 2021 International Conference on Management of Data},
     pages = {2846–2851},
     numpages = {6},
     keywords = {deep learning, text-to-SQL},
     location = {Virtual Event, China},
     series = {SIGMOD/PODS '21},
     html = {https://doi.org/10.1145/3448016.3457543},
     bibtex_show = {true},
     abbr = {SIGMOD}
   }

2. EDBT

   Deep Learning Approaches for Text-to-SQL Systems.

   Katsogiannis-Meimarakis, George, and Koutrika, Georgia

   In EDBT

   Bib

   @inproceedings{katsogiannis2021deep,
     title = {Deep Learning Approaches for Text-to-SQL Systems.},
     author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia},
     booktitle = {EDBT},
     pages = {710--713},
     year = {2021},
     bibtex_show = {true},
     abbr = {EDBT}
   }

3. 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}
   }

4. VLDB

   Let the Database Talk Back: Natural Language Explanations for SQL

   Eleftherakis, Stavroula, Gkini, Orest, and Koutrika, Georgia

   Proceedings of the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores, co-located with VLDB 2021

   Abs Bib HTML

   Database interaction is often characterized as a non-trivial and time consuming process due to user’s inexperience with the data or the query language. Therefore, there is a need for the databases to be able to "talk back" in order to assist the users during data exploration and eventually lead them to the desired results. In this paper, we tackle the problem of SQL-to-NL by extending the graph-based model of Logos. Our novel extensions include improvements in terms of the system’s translation capabilities and the fluency of the generated explanations. Finally, we report several challenges, highlighted by experiments on different user cases, i.e, astronomy and policy making.

   @article{eleftherakis2021NLforSQL,
     title = {Let the Database Talk Back: Natural Language Explanations for SQL},
     author = {Eleftherakis, Stavroula and Gkini, Orest and Koutrika, Georgia},
     journal = {Proceedings of the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores, co-located with VLDB 2021},
     publisher = {CEUR-WS.org},
     year = {2021},
     abbr = {VLDB},
     bibtex_show = {true},
     html = {http://ceur-ws.org/Vol-2929/paper3.pdf}
   }

5. SIGMOD

   An In-Depth Benchmarking of Text-to-SQL Systems

   Gkini, Orest, Belmpas, Theofilos, Koutrika, Georgia, and Ioannidis, Yannis

   Abs Bib HTML

   Text-to-SQL systems allow users to explore relational databases by posing free-form queries, alleviating the need for using structured languages, such as SQL. Although numerous systems have been developed so far, existing system evaluations lack in rigour. In this work, we build a text-to-SQL benchmark that covers different classes of queries, and we evaluate the effectiveness of several systems in the field. To evaluate system efficiency, we measure execution time and resource consumption for the different query classes. Our comprehensive evaluation aims at filling in a big gap in understanding the capabilities and boundaries of existing systems and it reveals several open challenges.

   @inbook{10.1145/3448016.3452836,
     author = {Gkini, Orest and Belmpas, Theofilos and Koutrika, Georgia and Ioannidis, Yannis},
     title = {An In-Depth Benchmarking of Text-to-SQL Systems},
     year = {2021},
     isbn = {9781450383431},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3448016.3452836},
     booktitle = {Proceedings of the 2021 International Conference on Management of Data},
     pages = {632–644},
     numpages = {13},
     abbr = {SIGMOD},
     bibtex_show = {true},
     html = {https://dl.acm.org/doi/10.1145/3448016.3452836}
   }

6. SIGMOD

   PyExplore: Query Recommendations for Data Exploration without Query Logs

   Glenis, Apostolos, and Koutrika, Georgia

   In Proceedings of the 2021 International Conference on Management of Data

   Bib HTML

   @inproceedings{glenis2021pyexplore,
     title = {PyExplore: Query Recommendations for Data Exploration without Query Logs},
     author = {Glenis, Apostolos and Koutrika, Georgia},
     booktitle = {Proceedings of the 2021 International Conference on Management of Data},
     pages = {2731--2735},
     abbr = {SIGMOD},
     bibtex_show = {true},
     html = {https://dl.acm.org/doi/abs/10.1145/3448016.3452762},
     url = {https://doi.org/10.1145/3448016.3452762},
     year = {2021}
   }

7. CoRR

   Fairness in Rankings and Recommendations: An Overview

   Pitoura, Evaggelia, Stefanidis, Kostas, and Koutrika, Georgia

   CoRR

   Bib

   @article{DBLP:journals/corr/abs-2104-05994,
     author = {Pitoura, Evaggelia and Stefanidis, Kostas and Koutrika, Georgia},
     title = {Fairness in Rankings and Recommendations: An Overview},
     journal = {CoRR},
     volume = {abs/2104.05994},
     year = {2021},
     url = {https://arxiv.org/abs/2104.05994},
     eprinttype = {arXiv},
     eprint = {2104.05994},
     timestamp = {Mon, 19 Apr 2021 16:45:47 +0200},
     biburl = {https://dblp.org/rec/journals/corr/abs-2104-05994.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true},
     abbr = {CoRR}
   }

8. CoRR

   INODE: Building an End-to-End Data Exploration System in Practice [Extended Vision]

   Amer-Yahia, Sihem, Koutrika, Georgia, Bastian, Frederic, Belmpas, Theofilos, Braschler, Martin, Brunner, Ursin, Calvanese, Diego, Fabricius, Maximilian, Gkini, Orest, Kosten, Catherine, Lanti, Davide, Litke, Antonis, Lücke-Tieke, Hendrik, Massucci, Francesco Alessandro, Farias, Tarcisio Mendes, Mosca, Alessandro, Multari, Francesco, Papadakis, Nikolaos, Papadopoulos, Dimitris, Patil, Yogendra, Personnaz, Aurélien, Rull, Guillem, Sima, Ana Claudia, Smith, Ellery, Skoutas, Dimitrios, Subramanian, Srividya, Xiao, Guohui, and Stockinger, Kurt

   CoRR

   Bib

   @article{DBLP:journals/corr/abs-2104-04194,
     author = {Amer{-}Yahia, Sihem and Koutrika, Georgia and Bastian, Frederic and Belmpas, Theofilos and Braschler, Martin and Brunner, Ursin and Calvanese, Diego and Fabricius, Maximilian and Gkini, Orest and Kosten, Catherine and Lanti, Davide and Litke, Antonis and L{\"{u}}cke{-}Tieke, Hendrik and Massucci, Francesco Alessandro and de Farias, Tarcisio Mendes and Mosca, Alessandro and Multari, Francesco and Papadakis, Nikolaos and Papadopoulos, Dimitris and Patil, Yogendra and Personnaz, Aur{\'{e}}lien and Rull, Guillem and Sima, Ana Claudia and Smith, Ellery and Skoutas, Dimitrios and Subramanian, Srividya and Xiao, Guohui and Stockinger, Kurt},
     title = {{INODE:} Building an End-to-End Data Exploration System in Practice
                      [Extended Vision]},
     journal = {CoRR},
     volume = {abs/2104.04194},
     year = {2021},
     url = {https://arxiv.org/abs/2104.04194},
     eprinttype = {arXiv},
     eprint = {2104.04194},
     timestamp = {Tue, 13 Apr 2021 16:46:17 +0200},
     biburl = {https://dblp.org/rec/journals/corr/abs-2104-04194.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true},
     abbr = {CoRR}
   }

9. Fairness-aware Methods in Rankings and Recommenders

   Pitoura, Evaggelia, Stefanidis, Kostas, and Koutrika, Georgia

   In 22nd IEEE International Conference on Mobile Data Management, MDM 2021, Toronto, ON, Canada, June 15-18, 2021

   Bib

   @inproceedings{DBLP:conf/mdm/PitouraSK21,
     author = {Pitoura, Evaggelia and Stefanidis, Kostas and Koutrika, Georgia},
     title = {Fairness-aware Methods in Rankings and Recommenders},
     booktitle = {22nd {IEEE} International Conference on Mobile Data Management, {MDM}
                      2021, Toronto, ON, Canada, June 15-18, 2021},
     pages = {1--4},
     publisher = {{IEEE}},
     year = {2021},
     url = {https://doi.org/10.1109/MDM52706.2021.00013},
     doi = {10.1109/MDM52706.2021.00013},
     timestamp = {Mon, 03 Jan 2022 22:37:29 +0100},
     biburl = {https://dblp.org/rec/conf/mdm/PitouraSK21.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true}
   }

10. ICDE

    Fairness in Rankings and Recommenders: Models, Methods and Research Directions

    Pitoura, Evaggelia, Stefanidis, Kostas, and Koutrika, Georgia

    In 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021

    Bib

    @inproceedings{DBLP:conf/icde/PitouraSK21,
      author = {Pitoura, Evaggelia and Stefanidis, Kostas and Koutrika, Georgia},
      title = {Fairness in Rankings and Recommenders: Models, Methods and Research
                       Directions},
      booktitle = {37th {IEEE} International Conference on Data Engineering, {ICDE} 2021,
                       Chania, Greece, April 19-22, 2021},
      pages = {2358--2361},
      publisher = {{IEEE}},
      year = {2021},
      url = {https://doi.org/10.1109/ICDE51399.2021.00265},
      doi = {10.1109/ICDE51399.2021.00265},
      timestamp = {Mon, 03 Jan 2022 22:33:27 +0100},
      biburl = {https://dblp.org/rec/conf/icde/PitouraSK21.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org},
      bibtex_show = {true},
      abbr = {ICDE}
    }

2020

1. NLonSpark: NL to SQL Translation on Top of Apache Spark

   Glenis, Apostolos, and Koutrika, Georgia

   HTML
2. Special issue on "Data Exploration in the Web 3.0 Age"

   Atzori, Maurizio, Koutrika, Georgia, Pes, Barbara, and Tanca, Letizia

   Future Gener. Comput. Syst.

   Bib

   @article{DBLP:journals/fgcs/AtzoriKPT20,
     author = {Atzori, Maurizio and Koutrika, Georgia and Pes, Barbara and Tanca, Letizia},
     title = {Special issue on "Data Exploration in the Web 3.0 Age"},
     journal = {Future Gener. Comput. Syst.},
     volume = {112},
     pages = {1177--1179},
     year = {2020},
     url = {https://doi.org/10.1016/j.future.2020.07.059},
     doi = {10.1016/j.future.2020.07.059},
     timestamp = {Tue, 18 Aug 2020 16:09:33 +0200},
     biburl = {https://dblp.org/rec/journals/fgcs/AtzoriKPT20.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true}
   }

3. EDBT

   Fairness in Rankings and Recommenders

   Pitoura, Evaggelia, Koutrika, Georgia, and Stefanidis, Kostas

   In Proceedings of the 23rd International Conference on Extending Database Technology, EDBT 2020, Copenhagen, Denmark, March 30 - April 02, 2020

   Bib

   @inproceedings{DBLP:conf/edbt/PitouraKS20,
     author = {Pitoura, Evaggelia and Koutrika, Georgia and Stefanidis, Kostas},
     editor = {Bonifati, Angela and Zhou, Yongluan and Salles, Marcos Antonio Vaz and B{\"{o}}hm, Alexander and Olteanu, Dan and Fletcher, George H. L. and Khan, Arijit and Yang, Bin},
     title = {Fairness in Rankings and Recommenders},
     booktitle = {Proceedings of the 23rd International Conference on Extending Database
                      Technology, {EDBT} 2020, Copenhagen, Denmark, March 30 - April 02,
                      2020},
     pages = {651--654},
     publisher = {OpenProceedings.org},
     year = {2020},
     url = {https://doi.org/10.5441/002/edbt.2020.86},
     doi = {10.5441/002/edbt.2020.86},
     timestamp = {Mon, 03 Jan 2022 22:22:58 +0100},
     biburl = {https://dblp.org/rec/conf/edbt/PitouraKS20.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true},
     abbr = {EDBT}
   }

4. RecSys

   Recommendations as Graph Explorations

   Kyriakidi, Marialena, Koutrika, Georgia, and Ioannidis, Yannis E.

   In RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22-26, 2020

   Bib

   @inproceedings{DBLP:conf/recsys/KyriakidiKI20,
     author = {Kyriakidi, Marialena and Koutrika, Georgia and Ioannidis, Yannis E.},
     editor = {Santos, Rodrygo L. T. and Marinho, Leandro Balby and Daly, Elizabeth M. and Chen, Li and Falk, Kim and Koenigstein, Noam and de Moura, Edleno Silva},
     title = {Recommendations as Graph Explorations},
     booktitle = {RecSys 2020: Fourteenth {ACM} Conference on Recommender Systems, Virtual
                      Event, Brazil, September 22-26, 2020},
     pages = {289--298},
     publisher = {{ACM}},
     year = {2020},
     url = {https://doi.org/10.1145/3383313.3412269},
     doi = {10.1145/3383313.3412269},
     timestamp = {Thu, 24 Sep 2020 10:19:44 +0200},
     biburl = {https://dblp.org/rec/conf/recsys/KyriakidiKI20.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     abbr = {RecSys},
     bibtex_show = {true}
   }

5. SIGMOD

   Analysis of Database Search Systems with THOR

   Belmpas, Theofilos, Gkini, Orest, and Koutrika, Georgia

   In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14-19, 2020

   Bib

   @inproceedings{DBLP:conf/sigmod/BelmpasGK20,
     author = {Belmpas, Theofilos and Gkini, Orest and Koutrika, Georgia},
     editor = {Maier, David and Pottinger, Rachel and Doan, AnHai and Tan, Wang{-}Chiew and Alawini, Abdussalam and Ngo, Hung Q.},
     title = {Analysis of Database Search Systems with {THOR}},
     booktitle = {Proceedings of the 2020 International Conference on Management of
                      Data, {SIGMOD} Conference 2020, online conference [Portland, OR, USA],
                      June 14-19, 2020},
     pages = {2681--2684},
     publisher = {{ACM}},
     year = {2020},
     url = {https://doi.org/10.1145/3318464.3384679},
     doi = {10.1145/3318464.3384679},
     timestamp = {Fri, 21 Aug 2020 17:36:54 +0200},
     biburl = {https://dblp.org/rec/conf/sigmod/BelmpasGK20.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true},
     abbr = {SIGMOD}
   }

2019

1. Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging

   Klyuchnikov, Nikita, Mottin, Davide, Koutrika, Georgia, Müller, Emmanuel, and Karras, Panagiotis

   In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019

   Bib

   @inproceedings{DBLP:conf/kdd/KlyuchnikovMKMK19,
     author = {Klyuchnikov, Nikita and Mottin, Davide and Koutrika, Georgia and M{\"{u}}ller, Emmanuel and Karras, Panagiotis},
     editor = {Teredesai, Ankur and Kumar, Vipin and Li, Ying and Rosales, R{\'{o}}mer and Terzi, Evimaria and Karypis, George},
     title = {Figuring out the User in a Few Steps: Bayesian Multifidelity Active
                      Search with Cokriging},
     booktitle = {Proceedings of the 25th {ACM} {SIGKDD} International Conference on
                      Knowledge Discovery {\&} Data Mining, {KDD} 2019, Anchorage, AK,
                      USA, August 4-8, 2019},
     pages = {686--695},
     publisher = {{ACM}},
     year = {2019},
     url = {https://doi.org/10.1145/3292500.3330893},
     doi = {10.1145/3292500.3330893},
     timestamp = {Thu, 14 Oct 2021 09:50:02 +0200},
     biburl = {https://dblp.org/rec/conf/kdd/KlyuchnikovMKMK19.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true}
   }

2. The Evolution of Search Over Structured Data: From SQL to Natural Language

   Koutrika, Georgia

   In Proceedings of the 8th International Conference on Data Science, Technology and Applications, DATA 2019, Prague, Czech Republic, July 26-28, 2019

   Bib

   @inproceedings{DBLP:conf/data/Koutrika19,
     author = {Koutrika, Georgia},
     editor = {Hammoudi, Slimane and Quix, Christoph and Bernardino, Jorge},
     title = {The Evolution of Search Over Structured Data: From {SQL} to Natural
                      Language},
     booktitle = {Proceedings of the 8th International Conference on Data Science, Technology
                      and Applications, {DATA} 2019, Prague, Czech Republic, July 26-28,
                      2019},
     pages = {5},
     publisher = {SciTePress},
     year = {2019},
     timestamp = {Wed, 18 Sep 2019 16:21:53 +0200},
     biburl = {https://dblp.org/rec/conf/data/Koutrika19.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     bibtex_show = {true}
   }