posted by organizer: dzissis || 7907 views || tracked by 33 users: [display]

STREAMS 2015 : Special Issue on Software Architectures and Systems for Real Time Data Stream Analytics

FacebookTwitterLinkedInGoogle

Link: http://www.journals.elsevier.com/journal-of-systems-and-software/call-for-papers/special-issue-on-software-architectures-and-systems/
 
When N/A
Where N/A
Submission Deadline Oct 31, 2015
Notification Due Jan 30, 2015
Categories    big data   data streams   machine learning   data analytics
 

Call For Papers

Topics of interest
Sensors are now being embedded into almost everything around us. Our surroundings are generating massive and potentially infinite sequences of data streams mainly sourced from non-stationary distributions in highly dynamic environments. Such streams of big data are produced by an increasing number of widely adopted systems including social media platforms, surveillance systems, sensor networks, telecommunication records, web logs, etc. Real-time data processing at massive scale is becoming more and more of a requirement for businesses. But information technology is challenged when called upon to decipher the complexity of these environments, discover knowledge in these streams of data and produce actionable intelligence. Stream Big Data has high volume and complex data types, but the true challenge lies in its high velocity characteristic, especially when concerning applications that require real-time data mining and machine learning.

This special issue will focus on this challenging area. Original and unpublished high-quality research results are solicited to explore various challenging topics, which include, but are not limited to:

System architectures supporting large-scale data stream fusion
Real world applications using steams of Big Data
Novel Architectures for efficiently mining streams of Big Data
Novel Algorithms for online machine learning and analytics
Data Streams & Cloud Computing
Storage systems for evolving, multilayer big graphs
Time-series processing systems under real-time constraints
Visualization of streaming big data
Infrastructures supporting large-scale and real-time data analytics
Cross-stream big data analytics for knowledge extraction
M2M data exchange
Low-power computing infrastructures tackling big data challenges
Data stream management systems from IOT platforms



Submission Guidelines

Articles can either be submitted directly to the special issue, or be selected for the special issue from papers submitted to RTStreams 2015: The 1st IEEE International Workshop on Real Time Data Stream Analytics (https://research.comnet.aalto.fi/BDSE2015/rtstreams2015/)

All submissions have to be prepared according to the Guide for Authors as published in the journal website at http://ees.elsevier.com/jss/. Authors should select “SI: STREAMS”, from the “Choose Article Type” pull-down menu during the submission process. All contributions must not have been previously published or be under consideration for publication elsewhere. A submission extended from a previous conference version has to contain at least 30% new material. Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.

Related Resources

DS-SAC 2022   DATA STREAMS TRACK - ACM SAC 2022
IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
RTAS 2022   28th IEEE Real Time Technology and Applications Symposium
ICML 2022   39th International Conference on Machine Learning
RTNS 2022   International Conference on Real-Time Networks and Systems
MLDM 2022   18th International Conference on Machine Learning and Data Mining
RTEST 2022   The CSI/CPSSI International Symposium on Real-Time and Embedded Systems and Technologies (RTEST)
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
NG-RES 2022   NG-RES: Workshop on Next Generation Real-Time Embedded Systems
ECRTS 2022   34th Euromicro Conference on Real-Time Systems