Data Streams Track at ACM SAC 2026

The ACM Symposium on Applied Computing (SAC) has served as a premier global forum for applied computer scientists, computer engineers, software engineers, and application developers. We are pleased to announce the Data Streams (DS) track for SAC 2026, sponsored by AI-BOOST.

About the Data Streams Track:

The DS track aims to be a vibrant meeting point and discussion forum for researchers engaged in all facets of Data Stream processing.
The exponential growth in Big Data information science and technology has introduced significant challenges, particularly concerning the complexity and volume of continuously generated data. Sources like the Internet of Things (IoT), Smart Cities, sensor networks, and customer click streams are good examples of data streams: ordered sequences of instances that are typically read once or a limited number of times, demanding efficient processing with constrained computing and storage. These data sources are characterized by their open-ended nature, high-speed flow, and non-stationary distributions.
Processing these ever-growing streaming datasets within reasonable timeframes requires innovative algorithms. Researchers from diverse fields, including data mining, machine learning, OLAP, and databases, are actively developing new approaches or adapting traditional algorithms to address these challenges. The prominence of data streams as a consolidated research topic at conferences like ICML, KDD, IJCAI, ICDM, and ECML further underscores its increasing importance.

Topics of Interest:

We invite original, unpublished research contributions related to algorithms, methods, and applications concerning big data streams and large-scale machine learning. Topics include, but are not restricted to:
Real-Time Analytics
Data Stream Models
Big Data Mining
Large-Scale Machine Learning
Languages for Stream Query
Continuous Queries
Clustering from Data Streams
Decision Trees from Data Streams
Association Rules from Data Streams
Decision Rules from Data Streams
Bayesian Networks from Data Streams
Feature Selection from Data Streams
Visualization Techniques for Data Streams
Incremental Online Learning Algorithms
Single-Pass Algorithms
Temporal, Spatial, and Spatio-Temporal Data Mining
Scalable Algorithms
Real-Time and Real-World Applications using Stream Data
Distributed Stream Mining
Social Network Stream Mining
Urban Computing, Smart Cities
Internet of Things (IoT)

Important Dates:

Paper Submission Deadline: September 26, 2025
Author Notification: October 31, 2025
Camera-Ready Copy Deadline: December 5, 2025

Paper Submission Guidelines:

Authors are invited to submit original papers on all topics related to data streams. All submissions must adhere to the ACM 2-column camera-ready format for publication in the symposium proceedings.
Double-Blind Review Process: ACM SAC employs a double-blind review process. Therefore, author names and addresses MUST NOT appear in the body of the submitted paper, and self-references should be made in the third person to facilitate anonymous review. All submitted papers must include the paper identification number provided by the eCMS system upon initial registration. This number must appear on the front page, above the paper's title.

Formatting and Length:
The paper length is 8 pages, with an option for 2 additional pages at an extra charge (maximum of 10 pages total).
Templates to support the required paper format for various document preparation systems can be found at: https://www.sigapp.org/sac/sac2026/authorkit.php
Submission guidelines must be strictly followed.
A paper cannot be submitted to more than one track.
Papers should be submitted in PDF format via the SAC 2026 Webpage.

Publication and Presentation:

Accepted papers in all categories will be published in the ACM SAC 2026 proceedings.
Paper registration is mandatory for the inclusion of papers, posters, or SRC abstracts in the conference proceedings.
An author or a proxy MUST present the work at SAC for it to be included in the ACM/IEEE Digital Library. No-shows for registered papers, posters, and SRC abstracts will result in their exclusion from the ACM/IEEE Digital Library.

Submission Portal:

Please submit your contribution via the SAC 2026 Webpage:
https://easychair.org/conferences/?conf=sac2026

We look forward to receiving your valuable contributions!