*ACM Symposium on Applied Computing *
The 37th ACM/SIGAPP Symposium on Applied Computing in Brno, Czech Republic
April 25 – April 29, 2022
www.sigapp.org/sac/sac2022/
*Data Streams Track *
abifet.github.io/SAC2022/
*Call for Papers *
The rapid development in Big Data information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. Many sources produce data continuously. Examples include the Internet of Things (IoT), Smart Cities, Urban Computing, sensor networks, wireless networks, radio frequency identification, health-care devices and information systems, customer click streams, telephone records, multimedia data, scientific data, sets of retail chain transactions, etc. These sources are called data streams. A data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, flowing at high-speed, and generated by non stationary distributions.
*TOPICS OF INTEREST *
We are looking for original, unpublished work related to algorithms, methods and applications on big data streams and large scale machine learning. Topics include (but are not restricted) to:
* Real-Time Analytics
* Big Data Mining
* Data Stream Models
* 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
* Neural Networks for Data Streams
* Feature Selection from Data Streams
* Visualization Techniques for Data Streams
* Incremental on-line Learning Algorithms
* Single-Pass Algorithms
* Temporal, spatial, and spatio-temporal data mining
* Scalable Algorithms
* Real-Time and Real-World Applications using Stream data
* Distributed and Social Stream Mining
* Urban Computing, Smart Cities
* Internet of Things (IoT)
* IMPORTANT DATES *
1. Submission deadline (Extended): October 24, 2021
2. Notification deadline: December 10, 2021
3. Camera-ready deadline: December 21, 2021
*PAPER SUBMISSION GUIDELINES *
Papers should be submitted in PDF. Authors are invited to submit original papers in all topics related to data streams. All papers should be submitted in ACM 2-column camera ready format for publication in the symposium proceedings. ACM SAC follows a double blind review process. Consequently, the author(s) name(s) and address(s) must NOT appear in the body of the submitted paper, and self-references should be in the third person. This is to facilitate double blind review required by ACM. All submitted papers must include the paper identification number provided by the eCMS system when the paper is first registered. The number must appear on the front page, above the title of the paper. Each submitted paper will be fully refereed and undergo a blind review process by at least three referees. The conference proceedings will be published by ACM. The maximum number of pages allowed for the final papers is 8 pages. There is a set of templates to support the required paper format for a number of document preparation systems at www.sigapp.org/sac/sac2022/authorkit.html
Important notice:
1. Please submit your contribution via SAC 2022 Webpage: www.softconf.com/m/sac2022/ 2. Paper registration is required, allowing the inclusion of the paper, poster, or SRC abstract in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for including the work in the ACM/IEEE digital library. No-show of registered papers, posters, and SRC abstracts will result in excluding them from the ACM/IEEE digital library.
If you encounter any problems with your submission, please contact the Program Coordinator.
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt
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