VLDB 2023: Call for Contributions - Industrial, Applications, and Experience Track

The Industrial Track of VLDB 2023 will cover all aspects of innovative commercial or industrial-strength data management systems and solutions. We also welcome novel real-world applications of data management systems and experience in applying recent research advances to real-world problems. We require that at least one of the authors has a non-academic affiliation.

Submission Guidelines

We accept full papers (up to 12 pages + references) and extended abstracts (up to 6 pages + references). The papers should be prepared using the VLDB 2023 paper formatting guidelines. Submissions must be made electronically, in pdf format, at the conference submission site: https://cmt3.research.microsoft.com/PVLDBv16_2023 . Submissions to the industrial track are required to include all author names and affiliations. Any changes to authorship after paper acceptance will require approval by the industrial track chairs. Authors are encouraged to contact the industrial track chairs if they need any clarification regarding the suitability of their work to this track.

Conflicts of Interest

It is the full responsibility of all authors of a paper to identify all and only the PC members with whom they have a conflict of interest (COI), as defined below. Papers with incorrect or incomplete COI information at the time when submission closes will be desk rejected. To minimize biases in the evaluation process, we use CMT's conflict management system, through which authors should flag conflicts with members of the Editorial Board. X and Y have a conflict of interest if any of the following applies:

Important Dates (All deadlines below are 5pm PT.)

Industrial Track Chairs

Abdul Quamar, Google
Yuanyan Tian, Microsoft Gray Systems Lab

Industrial Track PC Members

Amol Deshpande, University of Maryland at College Park
Beng Chin Ooi, NUS
Chuan Lei, Amazon Web Services
Danica Porobic, Oracle
Divy Agrawal, University of California, Santa Barbara
Eser Kandogan, Megagon Labs
Fabian Panse, Universit├Ąt Hamburg
Fatma Ozcan, Google
Feifei Li, Alibaba Group
Gustavo Alonso, ETHZ
Hanuma Kodavalla, Microsoft
Ihab Ilyas, U. of Waterloo and Apple
Jagan Sankaranarayanan, Google Inc
Karthik Ramachandra, Microsoft Azure SQL India
Lyublena Antova, Meta
Matthias Boehm, Technische Universit├Ąt Berlin
Mohamed Soliman, Apple
Norman May, SAP SE
Oktie Hassanzadeh, IBM Research
Rana Alotaibi, Microsoft Gray Systems Lab
Roger Barga, Amazon
Shaleen Deep, Microsoft Gray Systems Lab
Umesh Dayal, Hitachi America
Venkata Vamsikrishna Meduri, IBM Research, Almaden
Wolfram Wingerath, University of Oldenburg
Xiangyao You, University of Wisconsin-Madison
Yannis Katsis, IBM Research
Yingjun Wu, RisingWave Labs
Chris Douglas, UC Berkeley
Chen Luo, Snowflake
Liang Zhang, TigerGraph
Chang Ge, University of Minnesota
Bailu Ding, Microsoft Research
Juang Colmenares, LinkedIn
Yannis Chronis, Google
Vijayshankar Raman, Google
Carlo Curino, Microsoft
Yiwen Zhu, Gray Systems Lab
Lukas Rupprecht, Databricks
Fotis Psallidas, Gray Systems Lab