Introduction
The four main dimensions of Big Data are known as Volume, referring to the size of the data, Velocity, referring to the data that is generated rapidly, Veracity, referring to uncertainty in data and Variety, referring to data from different kinds of sources such as text, structure and video data. This workshop's focus is on the Velocity dimension of Big Data. The analytics of high velocity data has many applications, such as topic detection in Twitter, traffic control, network intrusion detection, etc. The difference compared with data that is stored on a disk is that real-time data may change its characteristics over time. However, decision support applications rely on the recency of their supporting data, hence, data generated at a high velocity needs to be processed ‘on the fly’. On the other hand, there are applications that are more interested in the actual change of the data, i.e. intrusion detection and network fault detection. Hence there is a need for computationally efficient real-time techniques that take changes of the data into consideration.
This workshop not only welcomes papers on data stream mining of high velocity data but also application from various domains, such as science, engineering, finance, web, etc. The workshop’s aim is to bring together researchers in this field to present their latest work, discuss challenges and future directions of research in Data Stream Mining.
Submitted extended abstracts (2 pages) will be reviewed. The authors of the best abstracts will be invited to submit full workshop papers, which will be further reviewed.
Proceedings
Accepted papers will be published in a special issue of the BCS SGAI publication Expert Update.
This workshop not only welcomes papers on data stream mining of high velocity data but also application from various domains, such as science, engineering, finance, web, etc. The workshop’s aim is to bring together researchers in this field to present their latest work, discuss challenges and future directions of research in Data Stream Mining.
Submitted extended abstracts (2 pages) will be reviewed. The authors of the best abstracts will be invited to submit full workshop papers, which will be further reviewed.
Proceedings
Accepted papers will be published in a special issue of the BCS SGAI publication Expert Update.
Topics of interest
- High Velocity Data Stream mining algorithms and techniques
- Big Data Streams
- Concept Drift Detection
- Real-time data mining applications
- Real-time event detection from streaming data.
Important dates
- Extended Abstract Submission (2 pages any format): extended until 30th of October 2016
- Invitation to submit full papers (8 pages):7th of October
- Submission deadline for full paper: TBA
- Notification of acceptance: TBA
- Camera ready papers and workshop registration: TBA
- Workshop: 13 December 2016
Workshop chair
- Frederic Stahl, University of Reading, UK
Programme committee
- Frederic Stahl (University of Reading, UK)
- Max Bramer (University of Portsmouth, UK)
- Mohamed Medhat Gaber (Robert Gordon University, UK)
- Joao Gomes (DataRobot, Singapore)
- Thien Le (University of Reading, UK)
Paper submission
Extended Abstracts can be directly send to Dr Frederic Stahl ([email protected])
Workshop Registration
One author per paper must present their work at the workshop and be registered for the workshop day of the AI2016 conference: http://www.bcs-sgai.org/ai2016/
Regular Rate £120
Student Rate £75
VAT is charged at 20%
Workshop Registration
One author per paper must present their work at the workshop and be registered for the workshop day of the AI2016 conference: http://www.bcs-sgai.org/ai2016/
Regular Rate £120
Student Rate £75
VAT is charged at 20%
