After the success of five international workshops co-located at IEEE Big Data, the MDPI Data Journal is dedicating a special issue to real-time stream analytics, stream mining, CER/CEP and stream data management in big data.
Data (ISSN 2306-5729) is a peer-reviewed open-access journal on data in science, with the aim of enhancing data transparency and reusability. The journal is now included in the Emerging Sources Citation Index – ESCI (Web of Science), Scopus, and Inspec (IET).
Data has received its first CiteScore 2.1, ranking Q2 in the Scopus category “Information Systems and Management” (Real-time CiteScore 3.2 based on CiteScoreTracker 2020).
We invite researchers in this field to submit papers about scalable online learning, incremental learning on stream processing infrastructures, complex event processing, and composite event recognition. We also encourage submissions on data stream management, data architecture using stream processing, and on Internet of Things (IoT) data streaming. Additionally, we appreciate submissions that deal with the usage of stream processing in new innovative architectures.
The full CFP can be found here : https://www.mdpi.com/journal/
The topics of interest include but are not limited to:
- New stream processing architecture for big data.
- Complex event processing (CEP) for big data, pattern matching engines for big data.
- Composite event recognition (CER).
- Stream reasoning.
- Scalable real-time decision algorithms.
- Scalable stream processing architecture, algorithms or models.
- Stream mining.
- Online and incremental learning.
- Stream SQL and other continuous query languages on big data frameworks.
- Data pipelines and data management with Streams.
- Stream ETL and real-time data warehouses.
- Stream mining and algorithms.
- Online and incremental learning and algorithms.
- New or innovative architecture patterns leveraging stream processing.
- IoT analytics
The deadline for the manuscript submission is March 1st 2021
Special Issue Editors
Sabri Skhiri, EURA NOVA, BE
Albert Bifet, Télécom Paris Tech, FR
Alessandro Margara, Politecnico di Milano, IT