VLDB 2024: Call for Contributions - Tutorials
VLDB 2024 invites submissions for tutorial proposals on all topics of potential interest to the conference attendees. Tutorial proposals should cover state-of-the-art research, development, and applications in specific data management related areas, and stimulate and facilitate future work. Tutorials on interdisciplinary directions, bridging scientific research and applied communities, novel and fast-growing directions, and significant applications are highly encouraged. We encourage tutorials in areas that may be different from the usual VLDB mainstream, but still very much related to VLDB mission and objectives of managing big data. We also encourage tutorials that apply advanced Machine Learning techniques to solve data management problems, and will be directly inviting a small number of tutorials from neighboring scientific communities. Tutorials should be targeted for a broad audience, and they must focus neither excessively, nor exclusively, on the authors’ own work.
Important Dates
All deadlines below are 5 PM Pacific Time.
- Submission deadline: April 15, 2024
- Notification: May 27, 2024
- Camera-ready abstract overview due: July 1, 2024
- Slides availability: August 24, 2024
Submission Guidelines
Tutorial submissions must be submitted electronically, in pdf format, at the conference submission site: https://cmt3.research.microsoft.com/PVLDBv17_2024.
Submissions should be formatted using the PVLDB style templates, with a maximum length of 4 pages, inclusive of ALL material.
Proposals should include:
- Title of the tutorial
- Names, affiliations and email addresses of the presenters
- Overview of tutorial, with justification of its relevance and timeliness
- Target audience and assumed background
- Related recent tutorials and how the proposed tutorial is different or novel compared to those/li>
- Scope and structure: enough detail to provide a sense of both the scope of material to be covered and the depth to which it will be covered
- Intended length of the tutorial (one session of 1.5 hours, or two sessions with a total of 3 hours on the same day). If the tutorial can be of either length, please identify which sections are included for each option
- References: include at least 10 primary and relevant bibliographic references on the core material of the tutorial
- Brief professional biographies of presenters, with a note on their background in the area of the tutorial
Accepted Tutorials
- LLM for Data Management, Guoliang Li (Tsinghua University)*; Xuanhe Zhou (Tsinghua); xinyang zhao (Tsinghua university)
- Native Distributed Databases: Problems, Challenges and Opportunities, Quanqing Xu (OceanBase, Ant Group )*; Chuanhui Yang (OceanBase); Aoying Zhou (East China Normal University)
- A Reproducible Tutorial on Reproducibility in Database Systems Research, Tim Fischer (Universität Tübingen); Denis Hirn (Universität Tübingen)*; Gokhan Kul (University of Massachusetts Dartmouth)
- Fairness in Preference Queries: Social Choice Theories Meet Data Management, Senjuti Basu Roy (New Jersey Institute of Technology)*; Baruch Schieber (NJIT); Nimrod Talmon (Ben Gurion University)
- Time-Series Anomaly Detection: Overview and New Trends, Qinghua Liu (The Ohio State University)*; Paul Boniol (Inria, Ecole normale supérieure); Themis Palpanas (Université Paris Cité); John Paparrizos (The Ohio State University)
- Consensus in Data Management With Use Cases in Edge-Cloud and Blockchain Systems, Faisal Nawab (University of California at Irvine)*; Mohammad Sadoghi (University of California, Davis)
- Efficient Training of Graph Neural Networks on Large Graphs, Yanyan Shen (Shanghai Jiao Tong University)*; Lei Chen (Hong Kong University of Science and Technology); Jingzhi Fang (HKUST); Xin Zhang (Hong Kong University of Science and Technology); Shihong Gao (The Hong Kong University of Science and Technology); Hongbo Yin (HKUST(GZ))
- Workload Placement on Heterogeneous CPU-GPU Systems, Marcos N. L. Carvalho (Universitat Politècnica de Catalunya)*; Alkis Simitsis (Athena Research Center); Anna Queralt (UPC); Oscar Romero (Universitat Politècnica de Catalunya)
- Spatial Query Optimization With Learning, Xin Zhang (University of California, Riverside)*; Ahmed Eldawy (University of California, Riverside)
- Composable Data Management: An Execution Overview, Pedro Pedreira (Meta Platforms Inc.)*; Deepak Majeti (Ahana); Orri Erling (Meta Platforms)
Tutorial Chairs
Li Xiong, Emory University, USA
Torsten Grust, Universität Tübingen
Tutorial Committee
Alberto Lerner, University of Fribourg, Switzerland
Alkis Simitsis, Athena Research Center, Greece
Amir Shaikhha, University of Edinburgh, UK
Chao Zhang, Tsinghua University, China
Chenhao Ma, The Chinese University of Hong Kong, Shenzen, China
Chuan Xiao, Osaka University, Nagoya University, Japan
Eamonn J. Keogh, UC Riverside, USA
Jana Giceva, TU Munich, Germany
Jelle Hellings, McMaster University, Canada
Jian Pei, Duke University, USA
Madhulika Mohanty, Inria Saclay, France
Maurice van Keulen, University of Twente, The Netherlands
Michael Grossniklaus, University of Konstanz, Germany
Ramon Antonio Rodriges Zalipynis, HSE University, Russia
Suyash Gupta, University of California, Berkeley, USA
Wei Wang, Hong Kong University of Science and Technology (Guangzhou), China
Xiaolan Wang, Meta, USA
Zoi Kaoudi, IT University of Copenhagen, Denmark