IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT2025)

Overview

BDCAT is a premier annual conference series aiming to provide a platform for researchers from both academia and industry to present new discoveries in the broad area of big data computing and applications. BDCAT 2025 will be the 12th Edition.

Dates

• Conference Dates: December 1st - 4th, 2025

Venue

• Location: Nantes, France
• University: University of Nantes

Co-located Conference

BDCAT 2025 is co-located with UCC2025 (The International Conference on Utility and Cloud Computing).

Topics of Interest

BDCAT focuses on the broad area of big data computing and applications. While specific topics are not detailed here, past conferences have covered areas related to big data technologies, algorithms, and their applications across various domains. Authors are encouraged to refer to the conference website for specific tracks and themes.

Submission Information

• Submit Your Paper: [Link to Submission Portal]
• Contribution Types: The conference typically accepts full papers, short papers, work-in-progress papers, and poster presentations. Please refer to the official Call for Papers for detailed types.
• Submission Guidelines: Specific formatting and length requirements will be provided on the official conference website. Authors should typically adhere to IEEE/ACM formatting standards.

Previous Editions

Previous events were held in various locations including London, UK (BDCAT 2014), Limassol, Cyprus (BDCAT 2015), Shanghai, China (BDCAT 2016), Austin, USA (BDCAT 2017), Zurich, Switzerland (BDCAT 2018), Auckland, New Zealand (BDCAT 2019), Leicester, UK (BDCAT 2020, 2021), Vancouver, USA (BDCAT 2022), Taormina, Italy (BDCAT 2023), and Sharjah, UAE (BDCAT 2024).

Contact Information

• General Enquiry: UCC-BDCAT@sharjah.ac.ae
• Newsletter Subscription: [Link to Newsletter Subscription]

Publication and Indexing

BDCAT proceedings are typically published by IEEE/ACM and archived in relevant digital libraries, with efforts made for indexing in major academic databases.