July 4, 2025
Panel Summary: Where Does Academic Database Research Go From Here?
Date: Thursday, June 26, 2025 Organizers: Eugene Wu, Raul Castro Fernandez Panelists: Overview As organizers, we aimed to foster a bottom-up discussion on the evolving role and future direction of academic database research. Motivated by seismic technological and budgetary shifts, particularly the rise of AI, we explored the comparative advantage of the academic database community. This advantage is critical […]
Eugene Wu
October 10, 2024
Where Does Database Research Go From Here?
The past few years of generative AI have upended research agendas across academia. Having just spent my sabbatical in the Bay Area, where the San Francisco fog is mixed with a tinge of forest fire and LLMs, I wanted to reflect on the role of the academic database research community within this sea change from the […]
Dimitris Sacharidis - Giorgos Giannopoulos - Loukas Kavouras
September 25, 2024
Auditing bias of recourse in classifiers (part II)
Introduction Fairness is a fundamental principle reflecting our innate sense of justice and equity. The essence of fairness lies in the equitable, unbiased and just treatment of all individuals. In our previous post (part I), we provided an introduction to the bias of recourse problem. In this post (part II), we describe our framework for […]
Paul Boniol - Themis Palpanas
July 16, 2024
Time Series Anomaly Detection
What it is, how it works, where we are, and where we are heading Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this post, we take a holistic view on anomaly […]
Dimitris Sacharidis - Giorgos Giannopoulos - Loukas Kavouras
June 19, 2024
Auditing bias of recourse in classifiers (part I)
Introduction Fairness is a fundamental principle reflecting our innate sense of justice and equity. The essence of fairness lies in the equitable, unbiased and just treatment of all individuals. Nevertheless, translating this principle to specific rules, people, and systems can adhere to is highly context specific, with context meaning, social and cultural circumstances, as well […]
Sarah Masud
August 20, 2025
To KG or not to KG, that is the question!
Even before retrieval augment generation (RAG) became a buzzword, researchers have been working on the infusion of knowledge bases with language models, allowing for better nudging of parametric knowledge in these models [1]. The source of this external knowledge can range from subject-relation-object tuples from knowledge graphs (KG) to summaries of Wikipedia pages. While studies […]
Sihem Amer-Yahia, Leilani Battle, Yifan Hu, Dominik Moritz, Aditya Parameswaran, Nikos Bikakis, Panos K. Chrysanthis, Guoliang Li, George Papastefanatos, Lingyun Yu
January 16, 2025
Data Exploration and Visual Analytics Challenges in AI Era
The International Workshop on Big Data Visual Exploration and Analytics (BigVis) is an annual event, which brings together scholars from the communities of Data Management & Mining, Information Visualization, Machine Learning and Human-Computer Interaction. The 7th BigVis event (BigVis 2024)1 was organized in conjunction with the 50th International Conference on Very Large Databases (VLDB 2024) […]
Xi Chen, Wei Hu, Arijit Khan, Shreya Shankar, Haofen Wang, Jianguo Wang, and Tianxing Wu
November 22, 2024
Large Language Models, Knowledge Graphs, and Vector Databases: Synergy and Opportunities for Data Management (A Report on the LLM+KG@VLDB24 Workshop’s Panel Discussion)
Introduction Large language models (LLMs) and vector databases (Vector DBs) are becoming two vital enablers of generative AI (GenAI), a form of artificial intelligence that learns from massive datasets to generate new data, showcasing human-like creativity in text, images to code, speech, and video. In particular, LLMs are currently revolutionizing the field of natural language […]