CLOSED: Call for Papers: Special Issue on Stream Data Learning and its Applications

IEEE TBD seeks submissions for this upcoming special issue.
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Submissions Due: 25 October 2023

Important Dates

  • Submission deadline: Oct 25, 2023
  • Author notification: Jan 25, 2024
  • Revised papers due: Feb 25, 2024
  • Final notification: Mar 25, 2024

Publication: TBA

Streaming analytics refers to the processing and analyzing of data records continuously in real-time
rather than in batches. The streaming data analytics market was estimated to be worth USD 15.4
billion in 2021. With a projected CAGR of 26.5% over the following five years, it will likely reach USD
50.1 billion. The streaming analytics industry is driven by increased digitalization and emerging
technologies such as big data, IoT, and AI to drive market growth. Companies use streaming analytics to provide insights into a wide range of activities, such as metering, server activity, geolocation of devices, or website clicks. For example, in e-commerce, one can analyze user clickstreams to optimize the shopping experience with real-time pricing, promotions, and inventory management; in financial services, one can analyze account activity to detect anomalous behavior in the data stream and generate a security alert for abnormal behavior.

Data traditionally is moved in batches. Batch processing often processes large volumes of data at the same time, with long periods of latency. While this can be an efficient way to handle large volumes of data, it doesn’t work with time-sensitive data, meant to be streamed, because that data can be stale by the time when it’s processed. Stream data analytics must be able to quickly and continuously process data so that organizations can react to problems and/or detect new trends which can help improve their performance. However, some challenges such as scalability, integration, fault-tolerance, timeliness, consistency, heterogeneity and incompleteness, load balancing, privacy issues, and accuracy arise from the nature of big data streams that must be dealt with.

This special issue aims to bring together researchers and practitioners from academia and industry to focus on stream data analytics and establishing new collaborations in these areas. The original research papers, state-of-the-art reviews, and use case studies are invited for publication in all areas of stream data analytics. Authors are solicited to contribute to the Research Topic by submitting articles that illustrate research results, projects, surveying works, and industrial experiences that describe significant advances in the areas of Computer Science & Engineering.


Topics include but are not limited to: 

  • Real-Time Analytics
  • Data Stream Models
  • Stream Data Mining
  • Languages for Stream Query
  • Continuous Queries
  • Clustering from Data Streams
  • Decision Trees from Data Streams
  • Association Rules from Data Streams
  • Decision-making from Data Streams
  • Feature Selection from Data Streams
  • Visualization Techniques for Data Streams
  • Incremental Online Learning Algorithms
  • Single-Pass Algorithms
  • Real-Time and Real-World Applications using Stream data
  • Distributed Stream Mining
  • Social Network Stream Mining

Submission Guidelines

For author information and guidelines on submission criteria, please visit the TBD’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.


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