Overview
The ACM Symposium on Applied Computing (SAC) is calling for papers for its Data Streams track for the 2026 edition. This track is designed to foster discussions among researchers focused on the challenges and innovations in data stream processing. Given the rapid expansion of big data technologies, this track is particularly significant for professionals in the AI and machine learning sectors.
Background & Relevance
Data streams represent a critical area of research in the context of big data, characterized by continuous flows of information from sources such as IoT devices, smart cities, and sensor networks. The ability to process these streams efficiently is essential for real-time analytics and decision-making. As the volume and complexity of data grow, researchers are increasingly tasked with developing novel algorithms and methods to handle these challenges effectively. The prominence of data streams in major conferences highlights its importance in the broader AI/ML landscape.
Key Details
- Paper Submission Deadline: September 26, 2025
- Author Notification: October 31, 2025
- Camera-Ready Copy Deadline: December 5, 2025
- Submission Portal: SAC 2026 Webpage
- Formatting Guidelines: ACM 2-column camera-ready format
- Paper Length: Up to 10 pages (8 pages + 2 additional pages for an extra charge)
Eligibility & Participation
This call for papers is open to all researchers and practitioners engaged in data stream processing. The track encourages contributions that advance the understanding and application of algorithms and methods in this domain.
Submission or Application Guidelines
- Prepare your manuscript according to the ACM 2-column camera-ready format.
- Ensure that your submission adheres to the double-blind review process by omitting author names and self-references in the body of the paper.
- Include the paper identification number provided upon registration in the eCMS system on the front page of your submission.
- Submit your paper in PDF format via the SAC 2026 Webpage.
- Note that a paper cannot be submitted to more than one track.
More Information
The Data Streams track at ACM SAC 2026 aims to bring together researchers from various fields, including data mining, machine learning, and databases, to discuss innovative approaches to data stream challenges. This track is particularly relevant as industries increasingly rely on real-time data for decision-making and operational efficiency.
Conclusion
Researchers are encouraged to contribute their original work to the Data Streams track at ACM SAC 2026. This is an excellent opportunity to share insights and advancements in the field of data stream processing. Interested participants should prepare their submissions in accordance with the guidelines and deadlines outlined above.
Category: CFP & Deadlines
Tags: data streams, big data, machine learning, real-time analytics, ACM SAC, data mining, IoT, urban computing, distributed stream mining, spatio-temporal data mining
