1. Aim and Scope
Nowadays, the big data has attracted attentions from more and more researchers. The big data is defined as the dataset whose size is beyond the processing ability of typical database or computers. The big data analytics is to automatically extract knowledge from large amounts of data. It can be seen as mining or processing of massive data, and “useful” information could be retrieved from large dataset. The properties of big data analytics can be concentrated in three parts: large volume, variety of different sources, and fast increasing speed, i.e., velocity. The algorithms should be effective to solve large-scale, dynamic the big data analytics problems.
Swarm intelligence (SI), which is based on a population of individuals, is a collection of nature-inspired searching techniques. To search a problem domain, a swarm intelligence algorithm processes a population of individuals. Each individual represents a potential solution of the problem being optimized. In swarm intelligence, an algorithm maintains and successively improves a population of potential solutions until some stopping condition is met. The solutions are initialized randomly in the search space, and are guided toward the better and better areas through the interaction among solutions over iterations.
The swarm intelligence algorithms have shown significant achievements on solving large scale, dynamical, and multi-objective problems. With the application of the swarm intelligence, more rapid and effective methods can be designed to solve big data analytics problems.
Topics
This special issue aims at fostering the latest development of Swarm Intelligence Techniques for Big Data analytics problems. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of Big Data analytics are very welcome for this Special Issue. Potential topics include, but are not limited to:
The use of swarm intelligence techniques such as:
1) Ant colony optimization
2) Artificial immune system
3) Brain Storm Optimization
4) Cultural algorithm
5) Differential Evolution
6) Fireworks Algorithm
7) Particle swarm optimization
in / for
Active learning on big data
Advertising on the Web
Anomaly detection in big data
Data size and feature space adaptation
Distributed learning techniques in uncertain environment
Distributed parallel computation
Feature selection/extraction in big data
Frequent Itemsets Analysis
Imbalance learning on big data
Incremental Learning
Link Analysis
Sample selection based on uncertainty
Uncertainty in cloud computing
Uncertainty modeling in learning from big data
Uncertainty techniques in big data classification / clustering
Massive data categorization / Clustering
Mining Data Streams
Mining Social-Network Graphs
Recommendation Systems
Reinforcement learning on big data
3. Submission
Manuscripts must be prepared according to the instructions of the “Guidelines for Submission” of the journal, available at: http://www.igi-global.com/journals/guidelines-for-submission.aspx.
Please submit your papers via emails to one of guest co-editors, Dr. Shi Cheng at shi.cheng#nottingham.edu.cn, or Dr. Ruibin Bai at ruibin.bai#nottingham.edu.cn. Submitted papers will be reviewed by at least three reviewers. The submission of a manuscript implies that it is the authors’ original unpublished work and has not being submitted for possible publication elsewhere.
January 1, 2014: Submission deadline.
March 1, 2014: Notice of the first round review.
April 1, 2014: Revision due
May 1, 2014: Final notice of acceptance/reject
June 1, 2014: Final manuscript due
4. Guest Editors
Dr. Shi Cheng, University of Nottingham Ningbo, China. Email:shi.cheng#nottingham.edu.cn
Dr. Ruibin Bai, University of Nottingham Ningbo, China. Email: ruibin.bai#nottingham.edu.cn
Dr. Kay Chen Tan, National University of Singapore. Email: eletankc#nus.edu.sg
Email: change # to @