BIFOLD at ICDE 2025
BIFOLD researchers will participate in the 41st IEEE International Conference on Data Engineering (ICDE 2025), which will be held from May 19 to May 23 in Hong Kong. They will contribute high-impact research in the areas of stream processing, data integration, and data-centric AI. The paper, titled “Incremental Stream Query Placement in Massively Distributed and Volatile Infrastructures,” authored by Ankit Chaudhary, Kaustubh Beedkar, Jeyhun Karimov, Felix Lang, Steffen Zeuch, and Volker Markl, has been awarded the ICDE 2025 Best Paper Award.
As one of the premier venues for data and information engineering, ICDE brings together researchers, industry experts, and thought leaders to explore the latest trends in designing, building, and managing advanced data-intensive systems and applications.
Below is an overview of BIFOLD’s contributions:
Title: Incremental Stream Query Placement in Massively Distributed and Volatile Infrastructures
Authors: Ankit Chaudhary, Kaustubh Beedkar, Jeyhun Karimov, Felix Lang, Steffen Zeuch, Volker Markl
Conference Track: 7.4: IoT Data Management; May 22 (Thu) 11:30 – 13:00 @Y 304
Link
Title: BLEND: A Unified Data Discovery System
Authors: Mahdi Esmailoghli, Christoph Schnell, Ziawasch Abedjan, Renée Miller
Conference Track: 3.4: Information Integration and Data Quality I; May 20 (Tue) 16:00 – 17:30 @Y 304
Link
Title: Chameleon: Adaptive and Scalable Stream Processing Over Sensor Sources
Authors: Dimitrios Giouroukis, Varun Pandey, Steffen Zeuch, Volker Markl
Conference Track: 7.2: Data Stream Systems and Edge Computing I; May 22 (Thu) 11:30 – 13:00 @Y 302
Link
Title: Incremental Stream Query Placement in Massively Distributed and Volatile Infrastructures
Authors: Ankit Chaudhary, Kaustubh Beedkar, Jeyhun Karimov, Felix Lang, Steffen Zeuch, Volker Markl
Conference Track: 7.4: IoT Data Management; May 22 (Thu) 11:30 – 13:00 @Y 304
Link
Workshop: First Workshop on Data-AI Systems (DAIS)
Speaker: Ziawasch Abedjan
Title: Navigating Disruption: The Impact of AI Technologies on Data Integration Research
Short Paper: Towards Regaining Control over Messy Machine Learning Pipelines [Vision].
Authors: Stefan Grafberger, Hao Chen, Olga Ovcharenko, Sebastian Schelter
Link
Title: Navigating Data Errors in Machine Learning Pipelines: Identify, Debug, and Learn
Authors: Bojan Karlas, Babak Salimi, Sebastian Schelter
Area Chair: Ziawasch Abedjan
Committee Members: Mahdi Esmailoghli, Stefan Grafberger, Steffen Zeuch